CA3183615A1 - Inventory value calculation method, stock value calculation device, computer equipment and storage medium - Google Patents
Inventory value calculation method, stock value calculation device, computer equipment and storage mediumInfo
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Abstract
The invention discloses a service commodity recommendation method, apparatus, computer device and storage medium, comprising: obtaining user data corresponding to service user, the user data carries commodity tag; performing data calculation on the user data based on a business strategy to obtain a set of service commodities to be recommended, the set of service commodities to be recommended includes at least one service commodity to be recommended, the service commodity to be recommended is used to serve the commodity corresponding to the commodity tag; sending the set of service commodities to be recommended to user device corresponding to service user, so that the user device displays each of service commodity to be recommended in the set of service commodities to be recommended. The present solution can improve recommendation performance.
Description
SERVICE COMMODITY RECOMMENDATION METHOD, APPARATUS, COMPUTER
DEVICE AND STORAGE MEDIUM
Technical Field [0001] The present disclosure relates to the computer technology field, particularly to a service commodity recommendation method, apparatus, computer device and storage medium.
Background
DEVICE AND STORAGE MEDIUM
Technical Field [0001] The present disclosure relates to the computer technology field, particularly to a service commodity recommendation method, apparatus, computer device and storage medium.
Background
[0002] Through analyzing the user commodity purchasing behavior data on the program website of shopping website, the program website of shopping website can recommend related commodities to increase commodity purchase rate. However, the current existing recommendation method is to make recommendation based on user behavior data or user purchasing records, and the same commodity type that the user has purchased can be recommended, for example, user has purchased or browsed air conditioner, then various types or brands of air conditioners are possibly recommended to the user again, but once the user has already purchased the air conditioner, then the air conditioner can never be needed again. In the application field of home appliance recommendation, this recommendation method cannot meet the user actual needs, resulting in poor recommendation results.
Summary
Summary
[0003] Based on this, it is necessary to provide a service commodity recommendation method, apparatus, computer device and storage medium, in the field of home appliance recommendation, according to commodity tag in user behavior data, service commodity that matches the commodity tag can be accurately recommended, the service commodity is used to serve commodity corresponding to the commodity tag, meet user actual needs and improve recommendation performance.
[0004] A service commodity recommendation method comprises:
[0005] Obtaining user data corresponding to service user, the user data carries commodity tag;
[0006] Performing data calculation on the user data based on a business strategy to obtain a set of Date Recue/Date Received 2023-01-30 service commodities to be recommended, the set of service commodities to be recommended includes at least one service commodity to be recommended, the service commodity to be recommended is used to serve the commodity corresponding to the commodity tag;
[0007] Sending the set of service commodities to be recommended to user device corresponding to service user, so that the user device displays each of service commodity to be recommended in the set of service commodities to be recommended.
[0008] In an embodiment, the user data includes commodity order data, user behavior data and user region data, the commodity order data carries commodity tag, the user behavior data carries user tag, the user region data carries user region tag, the preset business strategy includes hot sale commodity strategy, recommendation cycle strategy and region strategy, performing data calculation on the user data based on a business strategy to obtain a set of service commodities to be recommended, comprising:
determining a recommendation set of hot sale service commodities corresponding to the commodity tag according to the commodity order data and the hot sale commodity strategy;
determining a first service commodity recommendation set corresponding to the user tag according to the user behavior data and the recommendation cycle strategy; determining a second service commodity recommendation set corresponding to the region tag according to the user region data and the region strategy; calculating the recommended set of hot sale service commodities, the first recommended set of service commodities and the second recommended set of service commodities according to a preset weight distribution ratio to obtain a set of service commodities to be recommended.
determining a recommendation set of hot sale service commodities corresponding to the commodity tag according to the commodity order data and the hot sale commodity strategy;
determining a first service commodity recommendation set corresponding to the user tag according to the user behavior data and the recommendation cycle strategy; determining a second service commodity recommendation set corresponding to the region tag according to the user region data and the region strategy; calculating the recommended set of hot sale service commodities, the first recommended set of service commodities and the second recommended set of service commodities according to a preset weight distribution ratio to obtain a set of service commodities to be recommended.
[0009] In an embodiment, determining a recommendation set of hot sale service commodities corresponding to the commodity tag according to the commodity order data and the hot sale commodity strategy, comprising: searching for at least one matching candidate service commodity from the set of candidate service commodities according to the commodity tag; filtering out hot sale service commodities according to attention rate corresponding to each candidate service commodity to form a recommendation set of hot sale service commodities.
[00101 In an embodiment, determining a first service commodity recommendation set corresponding Date Recue/Date Received 2023-01-30 to the user tag according to the user behavior data and the recommendation cycle strategy, comprising:
obtaining a recommendation period corresponding to the recommendation cycle strategy; obtaining user usage time in the user behavior data; obtaining a recommendation set of associated first service commodities according to the recommendation period and the user usage time.
[0011] In an embodiment, determining a second service commodity recommendation set corresponding to the region tag according to the user region data and the region strategy, comprising:
obtaining service region corresponding to each candidate service commodity in the candidate service commodity set; obtaining a recommendation set of second service commodities according to the filtering of the user region data and each service region.
[0012] In an embodiment, calculating the recommended set of hot sale service commodities, the first recommended set of service commodities and the second recommended set of service commodities according to a preset weight distribution ratio to obtain a set of service commodities to be recommended, comprising: determining a first weight ratio, a second weight ratio and a third weight ratio according to a preset weight distribution ratio; performing sorting calculation according to the first weight ratio corresponding to the recommended set of hot sale service commodities, the second weight ratio corresponding to the first recommended set of service commodities, and the third weight ratio corresponding to the second recommended set of service commodities, obtaining a recommendation set of service commodities.
[0013] In an embodiment, obtaining user data corresponding to service user, comprising: receiving an operation corresponding to service user, obtaining user behavior data corresponding to the service user according to the operation; obtaining commodity tag according to the operation, obtaining matching commodity order data according to the commodity tag; obtaining the user region data corresponding to the service user through locating technology corresponding to the service user, forming user data according to the user behavior data, the commodity order data and the user region data.
[0014] A service commodity recommendation apparatus, wherein, the apparatus comprises:
Date Recue/Date Received 2023-01-30 [0015] An obtaining module configured to obtain user data corresponding to service user, the user data carries commodity tag;
[0016] A calculating module configured to perform data calculation on the user data based on a business strategy to obtain a set of service commodities to be recommended, the set of service commodities to be recommended includes at least one service commodity to be recommended, the service commodity to be recommended is used to serve the commodity corresponding to the commodity tag;
[0017] A sending module configured to send the set of service commodities to be recommended to user device corresponding to service user, so that the user device displays each of service commodity to be recommended in the set of service commodities to be recommended.
[0018] A computer device, including a memory, a processor and a computer program stored in the memory and run on the processor configured to achieve following steps when the processor executes the computer program:
[0019] Obtaining user data corresponding to service user, the user data carries commodity tag;
[0020] Performing data calculation on the user data based on a business strategy to obtain a set of service commodities to be recommended, the set of service commodities to be recommended includes at least one service commodity to be recommended, the service commodity to be recommended is used to serve the commodity corresponding to the commodity tag;
[0021] Sending the set of service commodities to be recommended to user device corresponding to service user, so that the user device displays each of service commodity to be recommended in the set of service commodities to be recommended.
[0022] A computer readable storage medium stored with a computer program configured to achieve following steps when the processor executes the computer program:
Date Recue/Date Received 2023-01-30 [0023] Obtaining user data corresponding to service user, the user data carries commodity tag;
[0024] Performing data calculation on the user data based on a business strategy to obtain a set of service commodities to be recommended, the set of service commodities to be recommended includes at least one service commodity to be recommended, the service commodity to be recommended is used to serve the commodity corresponding to the commodity tag;
[0025] Sending the set of service commodities to be recommended to user device corresponding to service user, so that the user device displays each of service commodity to be recommended in the set of service commodities to be recommended.
[0026] The above-mentioned service commodity recommendation method, apparatus, computer device and storage medium, obtaining user data corresponding to service user, the user data carries commodity tag, performing data calculation on the user data based on a business strategy to obtain a set of service commodities to be recommended, the set of service commodities to be recommended includes at least one service commodity to be recommended, the service commodity to be recommended is used to serve the commodity corresponding to the commodity tag, sending the set of service commodities to be recommended to user device corresponding to service user, so that the user device displays each of service commodity to be recommended in the set of service commodities to be recommended.
Therefore, according to the commodity tag in the user behavior data, the service commodity that matches the commodity tag can be accurately recommended, the service commodity is used to serve the commodity corresponding to the commodity tag, instead of recommending the similar commodity corresponding to the commodity tag to the user in the same way, so as to meet user actual needs and improve recommendation performance.
Drawing Description [0027] Figure 1 is an application process diagram of a service commodity recommendation method in an embodiment;
Date Recue/Date Received 2023-01-30 [0028] Figure 2 is a process diagram of a commodity recommendation method in an embodiment;
[0029] Figure 3 is a process diagram of user data calculation steps in an embodiment;
[0030] Figure 4 is a structural diagram of a service commodity recommendation apparatus in an embodiment;
[0031] Figure 5 is an internal structural diagram of a computer device in an embodiment.
Detailed Description [0032] In order to make clearer application purposes, technical solutions, and advantages, the present disclosure is further explained in detail with a particular embodiment thereof, and with reference to the drawings. It shall be appreciated that these descriptions are only intended to be illustrative, but not to limit the scope of the disclosure thereto.
[0033] The present application provides a service commodity recommendation method applied in the application environment as shown in Figure 1. Wherein, user device 102 can be terminal or server, the terminal can be but not limited to various personal computer, laptop, smart phone, tablet computer, portable wearable device or sub-server, server 104 can be an independent server or a server cluster composed of a plurality of servers to achieve.
[0034] Specifically, the server 104 obtains user data corresponding to service user, the user data carries commodity tag, and performs data calculation on the user data based on a business strategy to obtain a set of service commodities to be recommended, the set of service commodities to be recommended includes at least one service commodity to be recommended, the service commodity to be recommended is used to serve the commodity corresponding to the commodity tag, the server sends the set of service commodities to be recommended to the user device 102 corresponding to service user, and finally, the user device 102 displays each of service commodity to be recommended in the set of service commodities to be recommended.
Date Recue/Date Received 2023-01-30 [0035] In an embodiment, as shown in Figure 2, providing a service commodity recommendation method applied in the server of Figure 1 as an example, comprising following steps:
[0036] Step 202, obtaining user data corresponding to service user, the user data carries commodity tag.
[0037] Wherein, the user data is all data relevant with service user, service user is user who needs to be served at present, the user data can include but not limited to user behavior data, commodity order data and user region data, wherein, the user behavior data is data related to service user behavior, such as user browsing records, user search records, user payment records, etc., and commodity order data is commodity data related to service user, and the commodity order data can be the commodity data purchased by the service user, or the data of the commodity browsed by the service user, the commodity data carries commodity tag configured to identify commodity, the commodity tag can be delineated according to the overall commodity basic information, commodity identification, commodity attribute, etc., and is collectedly referred to as commodity tag. Wherein, the user region data is data related to the service user location, and the user region data can be obtained through the locating service of the device where the service user is located.
[0038] Specifically, determining the user identification corresponding to the service user, searching for the relevant user data through the user identification, and determining the commodity tag from the user data.
[0039] In an embodiment, obtaining user data corresponding to the service user, comprising: receiving the operation corresponding to the service user, collecting user behavior data corresponding to the service user according to the operation, obtaining the commodity tag according to the operation, obtaining the matched commodity order data according to the commodity tag, and obtaining the user region data corresponding to the service user through the locating technology corresponding to the service user, forming user data based on the user behavior data, commodity order data and user region data.
Date Recue/Date Received 2023-01-30 [0040] Specifically, the service user can operate on the relevant application interface, and the operation can be a click operation, a voice operation or a timing event trigger operation, after receiving the operation, the server collects the user behavior data corresponding to the service user according to the operation, the user behavior data can be the browsing behavior data of the service user, the search behavior data of the service user, etc., at the same time, at least one commodity tag can also be determined according to the service user operation, for example, determining what the searched commodity is from the search operation of the service user, further, searching for the corresponding commodity order data according to the commodity tag, the commodity order data here is the order data corresponding to the commodity tag currently owned by the service user, including currently purchased order data of the commodity tag, and previously purchased order data of the commodity tag, etc.
[0041] Wherein, obtaining the user region data related to the service user location through device locating service where the service user is located, including data such as country, province, city, region, street, street sign, etc., user data can be composed of at least one of user behavior data, commodity order data and user region data.
[0042] Step 204, performing data calculation on the user data based on a business strategy to obtain a set of service commodities to be recommended, the set of service commodities to be recommended includes at least one service commodity to be recommended, the service commodity to be recommended is used to serve the commodity corresponding to the commodity tag.
[0043] Wherein, the preset business strategy here is used to deduce the service commodity to be recommended according to user data, service commodity is a commodity with service nature, not a physical commodity, and the commodity corresponding to the commodity tag can be a physical commodity, the corresponding service commodity can be inferred based on the physical commodity related to the service user and recommended to the service user. The preset business strategy can be set in advance according to actual business requirements, actual commodity requirements or actual application scenarios, for example, the preset business strategy can consider factors such as user behavior habits and needs of service commodity in different regions, different communities, north-south Date Recue/Date Received 2023-01-30 climate, seasons, etc., wherein, the preset business strategy can include recommendation rules corresponding to different communities, and data calculation can be performed on user data through the recommendation rules described in the preset business strategy to obtain at least one commodity to be recommended to form a commodity set to be recommended.
[0044] Wherein, performing calculation on user data by the recommendation rules described in preset business strategy, specially can be, the user data include various types of data, obtaining the recommendation rules corresponding to various types of data in the preset business data, recommending the data according to the recommendation rules, and determining the set of service commodities to be recommended from the set of candidate service commodities.
[0045] Step 206, sending the set of service commodities to be recommended to user device corresponding to service user, so that the user device displays each of service commodity to be recommended in the set of service commodities to be recommended.
[0046] Specifically, the server sends the set of service commodities to be recommended to the user device corresponding to the service user, the user device can be the user terminal, further, after the user device receives the set of service commodities to be recommended, through the application display interface, displaying each to be recommended service commodity in the to be recommended service commodity set to user, so as to achieve the recommendation effect of the service commodity.
[0047] In the above-mentioned service commodity recommendation method, obtaining user data corresponding to service user, the user data carries commodity tag, performing data calculation on the user data based on a business strategy to obtain a set of service commodities to be recommended, the set of service commodities to be recommended includes at least one service commodity to be recommended, the service commodity to be recommended is used to serve the commodity corresponding to the commodity tag, sending the set of service commodities to be recommended to user device corresponding to service user, so that the user device displays each of service commodity to be recommended in the set of service commodities to be recommended. Therefore, according to the commodity tag in the user behavior data, the service commodity that matches the commodity tag can Date Recue/Date Received 2023-01-30 be accurately recommended, the service commodity is used to serve the commodity corresponding to the commodity tag, instead of recommending the similar commodity corresponding to the commodity tag to the user in the same way, so as to meet user actual needs and improve recommendation performance.
[0048] In an embodiment, as shown in Figure 3, the user data includes commodity order data, user behavior data and user region data, the commodity order data carries commodity tag, the user behavior data carries user tag, the user region data carries user region tag, the preset business strategy includes hot sale commodity strategy, recommendation cycle strategy and region strategy.
[0049] Performing data calculation on the user data based on a business strategy to obtain a set of service commodities to be recommended, comprising:
[0050] Step 302, determining a recommendation set of hot sale service commodities corresponding to the commodity tag according to the commodity order data and the hot sale commodity strategy.
[0051] Wherein, hot sale commodity strategy is one of preset business strategies, mainly filtering out the hot sale service commodity corresponding to the commodity tag from the candidate service commodity, and the hot sale service commodity is a service commodity with high user attention rate among the service commodities. Specifically, the commodity order data in the user data and the hot sale commodity strategy in the preset business strategy are obtained, the hot sale service commodity recommendation set corresponding to the commodity tag can be found through the hot sale commodity strategy and the commodity order data. Specifically, obtaining the commodity tag corresponding to the commodity order data, searching for the hot sale service commodity with user attentions according to the commodity tag, then forming a recommendation set of hot sale service commodities.
[0052] In an embodiment, determining a recommendation set of hot sale service commodities corresponding to the commodity tag according to the commodity order data and the hot sale commodity strategy, comprising: searching for at least one matching candidate service commodity from the set of candidate service commodities according to the commodity tag; filtering out hot sale service Date Recue/Date Received 2023-01-30 commodities according to attention rate corresponding to each candidate service commodity to form a recommendation set of hot sale service commodities.
[0053] Specifically, determining commodity tag according to the commodity order data, the commodity order data includes all data related to the commodity order, therefore, extracting the commodity tag from the commodity order data, the commodity tag can be a general commodity name, commodity basic information, commodity identification, etc., then searching for at least one matching candidate service commodity from the candidate service commodity set according to the commodity tag, the mapping relationship between the candidate service commodity and the commodity tag can be established in advance, therefore, searching for candidate service commodity that matches commodity tag through the mapping relationship, and according to the attention rate corresponding to each candidate service commodity, determining hot sale service commodity, forming a recommendation set of hot sale service commodities. Wherein, the attention rate corresponding to each candidate service commodity is calculated through the attention of the candidate service commodity relative to the user, and the attention behavior can be such as searching, browsing and purchasing, etc.
[0054] For example, in the field of home appliance, the commodity order data is air conditioner order data, and the commodity tag extracted from the commodity order data is air conditioner A, searching for the matched candidate service commodity based on the air conditioner A, such as cleaning service of air conditioner A, maintenance service of air conditioner A, replacement service of air conditioner A, etc., and then determining the hot sale service commodity according to the attention rate corresponding to each candidate service commodity: cleaning service of air conditioner A and maintenance service of air conditioner A, forming a recommendation set of hot sale service commodities.
[0055] Step 304, determining a first service commodity recommendation set corresponding to the user tag according to the user behavior data and the recommendation cycle strategy.
[0056] Wherein, recommendation cycle strategy is one of preset business strategies, mainly filtering out the first service commodity that meets the recommendation cycle and corresponds to the user tag Date Recue/Date Received 2023-01-30 from the candidate service commodities, in another words, the recommendation cycle strategy is after user purchases a certain physical commodity, when the related service commodities can be recommended. Wherein, user tag can user portrait, user basic information, user identification, etc., specifically, determining commodity tag and user tag according to user behavior data, obtaining the recommendation period in the recommendation cycle strategy, according to the user behavior data and the recommendation period, obtaining at least one first service commodity matching the recommendation period and corresponding to the user tag from the candidate service commodity set, forming a recommendation set of first service commodity.
[0057] In an embodiment, determining a first service commodity recommendation set corresponding to the user tag according to the user behavior data and the recommendation cycle strategy, comprising:
obtaining a recommendation period corresponding to the recommendation cycle strategy; obtaining user usage time in the user behavior data; obtaining a recommendation set of associated first service commodities according to the recommendation period and the user usage time.
[0058] Specifically, obtaining the preset recommendation period in the recommendation cycle strategy, extracting user usage time from user behavior data, the user usage time is the time when user starts to use the purchased commodity, the usage time can be commodity receiving time, commodity installation time, etc., then, the associated first service commodity recommendation set is ob ained by filtering from the candidate service commodity set through the recommendation time and the user usage time.
[0059] For example, in the filed of home appliance, user behavior data is behavior data related to service user A, the service user A has browsed the commodity data related to air conditioner A for many times, and the service user A has purchased commodity data related to air conditioner A half a year ago, the recommendation period in recommendation cycle strategy is half a year, therefore, filtering the first service commodity that needs to be recommended to the service user A after half a year, the first service commodity is related to air conditioner A, and the first service commodity can be used to serve air conditioner A, for example, maintenance service for air conditioner A every six months.
Date Recue/Date Received 2023-01-30 [0060] Step 306, determining a second service commodity recommendation set corresponding to the region tag according to the user region data and the region strategy.
[0061] Wherein, region strategy is also one of preset business strategies, mainly fileting out the second service commodity that meets the region tag and the corresponding commodity identification from the candidate service commodities, in another words, the region strategy considers geographical factors and filtering out service commodities that meet the geographical factors of user location, for example, the recommendation of service commodities in north and south regions can be different. Specifically, obtaining user region data, filtering out the second service commodity matching the region tag according to the user region data and the rules in the region strategy.
Wherein, the region tag can be region factor, climate factor, season factor, etc., and the rules in the region strategy can be preset according to business requirements, actual commodity requirements or actual application scenarios.
[0062] In an embodiment, determining a second service commodity recommendation set corresponding to the region tag according to the user region data and the region strategy, comprising:
obtaining service region corresponding to each candidate service commodity in the candidate service commodity set; obtaining a recommendation set of second service commodities according to the filtering of the user region data and each service region.
[0063] Specifically, obtaining the corresponding service region of each candidate service commodity in the candidate service commodity set, establishing the mapping relationship between candidate service commodity and service region in advance according to actual business needs, actual commodity requirements or actual application scenarios, then obtaining the second service commodity matching the user region data according to the mapping relationship, obtaining the second service commodity set.
[0064] For example, the service region corresponding to candidate service commodity A is Guangzhou, the service region corresponding to candidate service commodity B
is Beijing, and user region data is all data of Guangzhou, therefore, the filtered second service commodity is candidate service commodity A.
Date Recue/Date Received 2023-01-30 [0065] Step 308, calculating the recommended set of hot sale service commodities, the first recommended set of service commodities and the second recommended set of service commodities according to a preset weight distribution ratio to obtain a set of service commodities to be recommended.
[0066] Wherein, the preset weight distribution ratio can be based on actual business requirements, actual commodity requirements or actual application scenarios in advance, including first weight ratio corresponding to the hot sale service commodity recommendation set, second weight ratio corresponding to the first service commodity recommendation set, and the weight ratio corresponding to the second service commodity recommendation set, then, calculating the set of service commodities to be recommended according to the corresponding weight ratio of hot sale service commodity recommendation set, the first service commodity recommendation set and the second service commodity recommendation set.
[0067] In an embodiment, calculating the recommended set of hot sale service commodities, the first recommended set of service commodities and the second recommended set of service commodities according to a preset weight distribution ratio to obtain a set of service commodities to be recommended, comprising: determining a first weight ratio, a second weight ratio and a third weight ratio according to a preset weight distribution ratio; performing sorting calculation according to the first weight ratio corresponding to the recommended set of hot sale service commodities, the second weight ratio corresponding to the first recommended set of service commodities, and the third weight ratio corresponding to the second recommended set of service commodities, obtaining a recommendation set of service commodities.
[0068] For example, region strategy recommendation occupies for 50%: optimizing the recommendation sorting according to user region, the impact of sorting is according to the business backend configuration; recommendation period occupies for 30%: based on user behavior trajectory, including: search, collection, browsing, payment, etc., sorting the recommended commodities, the weight of the same commodity increases, the different commodities are sorted and graded according to the weight; hot sale commodity strategy occupies for 20%: based on the commodity being browsed, collected, added for purchasing, and purchased, the tags are calculated, users who have no purchase Date Recue/Date Received 2023-01-30 behavior with the same tag are recommended, the weight of the same commodities increases, the different commodities are sorted and graded according to the weight, obtaining a set of service commodities to be recommended.
[0069] In a specific scenario, the service commodity is recommended for non-cleaning commodity, based on statistical calculation of user behavior trajectory data, data calculating based on user browsing, purchasing, searching and collection behaviors in Suning APP/mini-program and Suning Helper APP/mini-program within a certain period (three months, half a year, one year), performing collaborative filtering process based on user according to user behavior data to obtain a sorted set of commodities recommended to user. The collaborative filtering is divided into user based and commodity based types, during the filtering process, performing the corresponding recommendation weighting process according to user tag, for user cold boot, the commodity tag is used to make recommendations, considering and recommending according to commodities that are more popular or are more popular in the locating area, summarizing the recommendation results, summarizing and filtering the recommended commodities with low conversion rate. Summarizing processing is divided into manual and automatic types, data can be exported manually and can be summarized by the business end or information end (spss and other software can be used for data analysis), through automatic summary, tagging the commodities with low conversion rate for cold boot recommendation, sequentially performing sorting operation of recommended commodities under cold boot.
[0070] Wherein, the cold boot refers to when user does not generate behavior trajectory data, then the user past transaction records in Suning or the data stored in the form of importing from other platforms are used to calculate and recommend, if user has no transaction records and no external order information importing operation, recommending service commodities according to hot sale commodities maintained in business strategy or region strategy(based on user locating operation). If user has no service address or location address, using commodity tag to perform cold boot sorting of commodity and commodity recommendation, summarizing the recommendation results, sorting in descending order for recommended commodities with low conversion rates under cold boot.
[0071] For another example, the service commodity is recommended for cleaning commodity, based Date Recue/Date Received 2023-01-30 on statistical calculation of user behavior trajectory data, data calculating based on user browsing, purchasing, searching and collection behaviors in Suning APP/mini-program and Suning Helper APP/mini-program within a certain period (three months, half a year, one year), wherein, private domain processing of physical home appliance, decoration tool and other commodities, if confidence interval reaches required level, user-based collaborative filtering is performed according user behavior data to obtain sorted set of commodities recommended to user and provide service commodities such as land reclamation and cleaning, formaldehyde removal according to recommendation. The collaborative filtering is divided into user based and commodity based types, during the filtering process, performing the corresponding recommendation weighting process according to user tag, for user cold boot, the commodity tag is used to make recommendations, considering and recommending according to commodities that are more popular or are more popular in the locating area, summarizing the recommendation results, summarizing and filtering the recommended commodities with low conversion rate. Summarizing processing is divided into manual and automatic types, data can be exported manually and can be summarized by the business end or information end (spss and other software can be used for data analysis), through automatic summary, tagging the commodities with low conversion rate for cold boot recommendation, sequentially performing sorting operation of recommended commodities under cold boot. It should be noted that the recommendations for cleaning commodity operations are all based on cleaning commodity pool (maintenance of cleaning commodity pool by business).
[0072] What should be noted is although the steps of the above-mentioned process diagrams are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly provided instruction in this article, there is no strict order in which these steps can be performed, and they can be performed in any other orders. In addition, at least partial steps of the above-mentioned Figures can include more sub steps or multiple stages, these sub steps or stages are not necessarily completed at the same time but can be executed in different time, the execution order of these sub steps or stages is also not necessarily in sequence order but can be performed alternately with the other steps or sub steps of other steps or at least one part of the other stages.
[0073] In an embodiment, a service commodity recommendation apparatus 400, as shown in Figure Date Recue/Date Received 2023-01-30 4, comprising: obtaining module 402, calculating module 404, and sending module 406, wherein:
[0074] An obtaining module 402 configured to obtain user data corresponding to service user, the user data carries commodity tag.
[0075] A calculating module 404 configured to perform data calculation on the user data based on a business strategy to obtain a set of service commodities to be recommended, the set of service commodities to be recommended includes at least one service commodity to be recommended, the service commodity to be recommended is used to serve the commodity corresponding to the commodity tag.
[0076] A sending module 406 configured to send the set of service commodities to be recommended to user device corresponding to service user, so that the user device displays each of service commodity to be recommended in the set of service commodities to be recommended.
[0077] In an embodiment, the user data includes commodity order data, user behavior data and user region data, the commodity order data carries commodity tag, the user behavior data carries user tag, the user region data carries user region tag, the preset business strategy includes hot sale commodity strategy, recommendation cycle strategy and region strategy, calculating module 404 determines a recommendation set of hot sale service commodities corresponding to the commodity tag according to the commodity order data and the hot sale commodity strategy, determines a first service commodity recommendation set corresponding to the user tag according to the user behavior data and the recommendation cycle strategy, determines a second service commodity recommendation set corresponding to the region tag according to the user region data and the region strategy, and calculates the recommended set of hot sale service commodities, the first recommended set of service commodities and the second recommended set of service commodities according to a preset weight distribution ratio to obtain a set of service commodities to be recommended.
[0078] In an embodiment, the calculating module 404 searches for at least one matching candidate service commodity from the set of candidate service commodities according to the commodity tag, Date Recue/Date Received 2023-01-30 filtering out hot sale service commodities according to attention rate corresponding to each candidate service commodity to form a recommendation set of hot sale service commodities.
[0079] In an embodiment, the calculating module 404 obtains a recommendation period corresponding to the recommendation cycle strategy, obtains user usage time in the user behavior data, and obtains a recommendation set of associated first service commodities according to the recommendation period and the user usage time.
[0080] In an embodiment, the calculating module 404 obtains service region corresponding to each candidate service commodity in the candidate service commodity set, and obtains a recommendation set of second service commodities according to the filtering of the user region data and each service region.
[0081] In an embodiment, the calculating module 404 determines a first weight ratio, a second weight ratio and a third weight ratio according to a preset weight distribution ratio, performs sorting calculation according to the first weight ratio corresponding to the recommended set of hot sale service commodities, the second weight ratio corresponding to the first recommended set of service commodities, and the third weight ratio corresponding to the second recommended set of service commodities, and obtains a recommendation set of service commodities.
[0082] In an embodiment, the obtaining module 402 receives an operation corresponding to service user, obtains user behavior data corresponding to the service user according to the operation, obtains commodity tag according to the operation, obtaining matching commodity order data according to the commodity tag; and obtains the user region data corresponding to the service user through locating technology corresponding to the service user, forming user data according to the user behavior data, the commodity order data and the user region data.
[0083] For the specific limitation of a service commodity recommendation apparatus can refer to the above-mentioned service commodity recommendation method, which will not be repeated here. Each module in the above-mentioned service commodity recommendation apparatus can be achieved fully Date Recue/Date Received 2023-01-30 or partly by software, hardware, and their combinations. The above modules can be embedded in the processor or independent of the processor in computer device and can store in the memory of computer device in form of software, so that the processor can call and execute the operations corresponding to the above modules.
[0084] In an embodiment, a computer device is provided, the computer device can be a server whose internal structure diagram is shown in Figure 5. The computer device includes a processor, a memory, a network interface, and a database connected through a system bus. The processor of the computer device is configured to provide calculation and control capabilities. The memory of the computer device includes non-volatile storage medium and internal memory. The memory of non-volatile storage medium has an operation system, computer programs and database. The internal memory provides an environment for the operation system and computer program running in a non-volatile storage medium.
The network interface of the computer device is used to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement a service commodity recommendation method.
[0085] The skilled in the art can understand that the structure shown in Figure 5 is only partial structural diagram related this application solution and not constitute limitation to the computer device applied on the current application solution, the specific computer device can include more or less components than what is shown in the figure, or combinations of some components or different components to what is shown in the figure.
[0086] In an embodiment, a computer device is provided, including a memory, a processor and a computer program stored in the memory and ran on the processor configured to achieve the following steps when the processor executes the computer program: obtaining user data corresponding to service user, the user data carries commodity tag; performing data calculation on the user data based on a business strategy to obtain a set of service commodities to be recommended, the set of service commodities to be recommended includes at least one service commodity to be recommended, the service commodity to be recommended is used to serve the commodity corresponding to the commodity tag; sending the set of service commodities to be recommended to user device corresponding to service Date Recue/Date Received 2023-01-30 user, so that the user device displays each of service commodity to be recommended in the set of service commodities to be recommended.
[0087] In an embodiment, the user data includes commodity order data, user behavior data and user region data, the commodity order data carries commodity tag, the user behavior data carries user tag, the user region data carries user region tag, the preset business strategy includes hot sale commodity strategy, recommendation cycle strategy and region strategy, performing data calculation on the user data based on a business strategy to obtain a set of service commodities to be recommended, comprising:
determining a recommendation set of hot sale service commodities corresponding to the commodity tag according to the commodity order data and the hot sale commodity strategy;
determining a first service commodity recommendation set corresponding to the user tag according to the user behavior data and the recommendation cycle strategy; determining a second service commodity recommendation set corresponding to the region tag according to the user region data and the region strategy; calculating the recommended set of hot sale service commodities, the first recommended set of service commodities and the second recommended set of service commodities according to a preset weight distribution ratio to obtain a set of service commodities to be recommended.
[0088] In an embodiment, the processor executes computer program to achieve following steps:
searching for at least one matching candidate service commodity from the set of candidate service commodities according to the commodity tag; filtering out hot sale service commodities according to attention rate corresponding to each candidate service commodity to form a recommendation set of hot sale service commodities.
[0089] In an embodiment, the processor executes computer program to achieve following steps:
obtaining a recommendation period corresponding to the recommendation cycle strategy; obtaining user usage time in the user behavior data; obtaining a recommendation set of associated first service commodities according to the recommendation period and the user usage time.
[0090] In an embodiment, the processor executes computer program to achieve following steps:
obtaining service region corresponding to each candidate service commodity in the candidate service Date Recue/Date Received 2023-01-30 commodity set; obtaining a recommendation set of second service commodities according to the filtering of the user region data and each service region.
[0091] In an embodiment, the processor executes computer program to achieve following steps:
determining a first weight ratio, a second weight ratio and a third weight ratio according to a preset weight distribution ratio; performing sorting calculation according to the first weight ratio corresponding to the recommended set of hot sale service commodities, the second weight ratio corresponding to the first recommended set of service commodities, and the third weight ratio corresponding to the second recommended set of service commodities, obtaining a recommendation set of service commodities.
[0092] In an embodiment, the processor executes computer program to achieve following steps:
receiving an operation corresponding to service user, obtaining user behavior data corresponding to the service user according to the operation; obtaining commodity tag according to the operation, obtaining matching commodity order data according to the commodity tag; obtaining the user region data corresponding to the service user through locating technology corresponding to the service user, forming user data according to the user behavior data, the commodity order data and the user region data.
[0093] In an embodiment, a computer readable storage medium is provided, the medium stored with computer program and the processor performs the following steps when executing the computer program: obtaining user data corresponding to service user, the user data carries commodity tag;
performing data calculation on the user data based on a business strategy to obtain a set of service commodities to be recommended, the set of service commodities to be recommended includes at least one service commodity to be recommended, the service commodity to be recommended is used to serve the commodity corresponding to the commodity tag; sending the set of service commodities to be recommended to user device corresponding to service user, so that the user device displays each of service commodity to be recommended in the set of service commodities to be recommended.
[0094] In an embodiment, the user data includes commodity order data, user behavior data and user Date Recue/Date Received 2023-01-30 region data, the commodity order data carries commodity tag, the user behavior data carries user tag, the user region data carries user region tag, the preset business strategy includes hot sale commodity strategy, recommendation cycle strategy and region strategy, performing data calculation on the user data based on a business strategy to obtain a set of service commodities to be recommended, comprising:
determining a recommendation set of hot sale service commodities corresponding to the commodity tag according to the commodity order data and the hot sale commodity strategy;
determining a first service commodity recommendation set corresponding to the user tag according to the user behavior data and the recommendation cycle strategy; determining a second service commodity recommendation set corresponding to the region tag according to the user region data and the region strategy; calculating the recommended set of hot sale service commodities, the first recommended set of service commodities and the second recommended set of service commodities according to a preset weight distribution ratio to obtain a set of service commodities to be recommended.
[0095] In an embodiment, the processor executes computer program to achieve following steps:
searching for at least one matching candidate service commodity from the set of candidate service commodities according to the commodity tag; filtering out hot sale service commodities according to attention rate corresponding to each candidate service commodity to form a recommendation set of hot sale service commodities.
[0096] In an embodiment, the processor executes computer program to achieve following steps:
obtaining a recommendation period corresponding to the recommendation cycle strategy; obtaining user usage time in the user behavior data; obtaining a recommendation set of associated first service commodities according to the recommendation period and the user usage time.
[0097] In an embodiment, the processor executes computer program to achieve following steps:
obtaining service region corresponding to each candidate service commodity in the candidate service commodity set; obtaining a recommendation set of second service commodities according to the filtering of the user region data and each service region.
[0098] In an embodiment, the processor executes computer program to achieve following steps:
Date Recue/Date Received 2023-01-30 determining a first weight ratio, a second weight ratio and a third weight ratio according to a preset weight distribution ratio; performing sorting calculation according to the first weight ratio corresponding to the recommended set of hot sale service commodities, the second weight ratio corresponding to the first recommended set of service commodities, and the third weight ratio corresponding to the second recommended set of service commodities, obtaining a recommendation set of service commodities.
[0099] In an embodiment, the processor executes computer program to achieve following steps:
receiving an operation corresponding to service user, obtaining user behavior data corresponding to the service user according to the operation; obtaining commodity tag according to the operation, obtaining matching commodity order data according to the commodity tag; obtaining the user region data corresponding to the service user through locating technology corresponding to the service user, forming user data according to the user behavior data, the commodity order data and the user region data.
[0100] The skilled in the art can understand that all or partial of procedures from the above-mentioned methods can be performed by computer program instructions through related hardware, the mentioned computer program can be stored in a non-volatile material computer readable storage medium, this computer can include various embodiment procedures from the abovementioned methods when execution. Any reference to the memory, the storage, the database, or the other media used in each embodiment provided in current application can include non-volatile and/or volatile memory. Non-volatile memory can include read-only memory (ROM), programable ROM (PROM), electrically programmable ROM (EPRPMD), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an instruction but not limited to, RAM is available in many forms such as static RAM (SRAM), dynamic RAM (DRAMD), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SRAM (ESDRAM), synchronal link (Synchlink) DRAM (SLDRAM), memory bus (Rambus), direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM
(RDRAM), etc.
Date Recue/Date Received 2023-01-30 [0101] The technical features of the above-mentioned embodiments can be randomly combined, for concisely statement, not all possible combinations of technical feature in the abovementioned embodiments are described. However, if there are no conflicts in the combinations of these technical features, it shall be within the scope of this description.
[0102] The above-mentioned embodiments are only several embodiments in this disclosure and the description is more specific and detailed but cannot be understood as the limitation of the scope of the invention patent. Evidently those ordinary skilled in the art can make various modifications and variations to the disclosure without departing from the spirit and scope of the disclosure. Therefore, the appended claims are intended to be construed as encompassing the described embodiment and all the modifications and variations coming into the scope of the disclosure.
Date Recue/Date Received 2023-01-30
[00101 In an embodiment, determining a first service commodity recommendation set corresponding Date Recue/Date Received 2023-01-30 to the user tag according to the user behavior data and the recommendation cycle strategy, comprising:
obtaining a recommendation period corresponding to the recommendation cycle strategy; obtaining user usage time in the user behavior data; obtaining a recommendation set of associated first service commodities according to the recommendation period and the user usage time.
[0011] In an embodiment, determining a second service commodity recommendation set corresponding to the region tag according to the user region data and the region strategy, comprising:
obtaining service region corresponding to each candidate service commodity in the candidate service commodity set; obtaining a recommendation set of second service commodities according to the filtering of the user region data and each service region.
[0012] In an embodiment, calculating the recommended set of hot sale service commodities, the first recommended set of service commodities and the second recommended set of service commodities according to a preset weight distribution ratio to obtain a set of service commodities to be recommended, comprising: determining a first weight ratio, a second weight ratio and a third weight ratio according to a preset weight distribution ratio; performing sorting calculation according to the first weight ratio corresponding to the recommended set of hot sale service commodities, the second weight ratio corresponding to the first recommended set of service commodities, and the third weight ratio corresponding to the second recommended set of service commodities, obtaining a recommendation set of service commodities.
[0013] In an embodiment, obtaining user data corresponding to service user, comprising: receiving an operation corresponding to service user, obtaining user behavior data corresponding to the service user according to the operation; obtaining commodity tag according to the operation, obtaining matching commodity order data according to the commodity tag; obtaining the user region data corresponding to the service user through locating technology corresponding to the service user, forming user data according to the user behavior data, the commodity order data and the user region data.
[0014] A service commodity recommendation apparatus, wherein, the apparatus comprises:
Date Recue/Date Received 2023-01-30 [0015] An obtaining module configured to obtain user data corresponding to service user, the user data carries commodity tag;
[0016] A calculating module configured to perform data calculation on the user data based on a business strategy to obtain a set of service commodities to be recommended, the set of service commodities to be recommended includes at least one service commodity to be recommended, the service commodity to be recommended is used to serve the commodity corresponding to the commodity tag;
[0017] A sending module configured to send the set of service commodities to be recommended to user device corresponding to service user, so that the user device displays each of service commodity to be recommended in the set of service commodities to be recommended.
[0018] A computer device, including a memory, a processor and a computer program stored in the memory and run on the processor configured to achieve following steps when the processor executes the computer program:
[0019] Obtaining user data corresponding to service user, the user data carries commodity tag;
[0020] Performing data calculation on the user data based on a business strategy to obtain a set of service commodities to be recommended, the set of service commodities to be recommended includes at least one service commodity to be recommended, the service commodity to be recommended is used to serve the commodity corresponding to the commodity tag;
[0021] Sending the set of service commodities to be recommended to user device corresponding to service user, so that the user device displays each of service commodity to be recommended in the set of service commodities to be recommended.
[0022] A computer readable storage medium stored with a computer program configured to achieve following steps when the processor executes the computer program:
Date Recue/Date Received 2023-01-30 [0023] Obtaining user data corresponding to service user, the user data carries commodity tag;
[0024] Performing data calculation on the user data based on a business strategy to obtain a set of service commodities to be recommended, the set of service commodities to be recommended includes at least one service commodity to be recommended, the service commodity to be recommended is used to serve the commodity corresponding to the commodity tag;
[0025] Sending the set of service commodities to be recommended to user device corresponding to service user, so that the user device displays each of service commodity to be recommended in the set of service commodities to be recommended.
[0026] The above-mentioned service commodity recommendation method, apparatus, computer device and storage medium, obtaining user data corresponding to service user, the user data carries commodity tag, performing data calculation on the user data based on a business strategy to obtain a set of service commodities to be recommended, the set of service commodities to be recommended includes at least one service commodity to be recommended, the service commodity to be recommended is used to serve the commodity corresponding to the commodity tag, sending the set of service commodities to be recommended to user device corresponding to service user, so that the user device displays each of service commodity to be recommended in the set of service commodities to be recommended.
Therefore, according to the commodity tag in the user behavior data, the service commodity that matches the commodity tag can be accurately recommended, the service commodity is used to serve the commodity corresponding to the commodity tag, instead of recommending the similar commodity corresponding to the commodity tag to the user in the same way, so as to meet user actual needs and improve recommendation performance.
Drawing Description [0027] Figure 1 is an application process diagram of a service commodity recommendation method in an embodiment;
Date Recue/Date Received 2023-01-30 [0028] Figure 2 is a process diagram of a commodity recommendation method in an embodiment;
[0029] Figure 3 is a process diagram of user data calculation steps in an embodiment;
[0030] Figure 4 is a structural diagram of a service commodity recommendation apparatus in an embodiment;
[0031] Figure 5 is an internal structural diagram of a computer device in an embodiment.
Detailed Description [0032] In order to make clearer application purposes, technical solutions, and advantages, the present disclosure is further explained in detail with a particular embodiment thereof, and with reference to the drawings. It shall be appreciated that these descriptions are only intended to be illustrative, but not to limit the scope of the disclosure thereto.
[0033] The present application provides a service commodity recommendation method applied in the application environment as shown in Figure 1. Wherein, user device 102 can be terminal or server, the terminal can be but not limited to various personal computer, laptop, smart phone, tablet computer, portable wearable device or sub-server, server 104 can be an independent server or a server cluster composed of a plurality of servers to achieve.
[0034] Specifically, the server 104 obtains user data corresponding to service user, the user data carries commodity tag, and performs data calculation on the user data based on a business strategy to obtain a set of service commodities to be recommended, the set of service commodities to be recommended includes at least one service commodity to be recommended, the service commodity to be recommended is used to serve the commodity corresponding to the commodity tag, the server sends the set of service commodities to be recommended to the user device 102 corresponding to service user, and finally, the user device 102 displays each of service commodity to be recommended in the set of service commodities to be recommended.
Date Recue/Date Received 2023-01-30 [0035] In an embodiment, as shown in Figure 2, providing a service commodity recommendation method applied in the server of Figure 1 as an example, comprising following steps:
[0036] Step 202, obtaining user data corresponding to service user, the user data carries commodity tag.
[0037] Wherein, the user data is all data relevant with service user, service user is user who needs to be served at present, the user data can include but not limited to user behavior data, commodity order data and user region data, wherein, the user behavior data is data related to service user behavior, such as user browsing records, user search records, user payment records, etc., and commodity order data is commodity data related to service user, and the commodity order data can be the commodity data purchased by the service user, or the data of the commodity browsed by the service user, the commodity data carries commodity tag configured to identify commodity, the commodity tag can be delineated according to the overall commodity basic information, commodity identification, commodity attribute, etc., and is collectedly referred to as commodity tag. Wherein, the user region data is data related to the service user location, and the user region data can be obtained through the locating service of the device where the service user is located.
[0038] Specifically, determining the user identification corresponding to the service user, searching for the relevant user data through the user identification, and determining the commodity tag from the user data.
[0039] In an embodiment, obtaining user data corresponding to the service user, comprising: receiving the operation corresponding to the service user, collecting user behavior data corresponding to the service user according to the operation, obtaining the commodity tag according to the operation, obtaining the matched commodity order data according to the commodity tag, and obtaining the user region data corresponding to the service user through the locating technology corresponding to the service user, forming user data based on the user behavior data, commodity order data and user region data.
Date Recue/Date Received 2023-01-30 [0040] Specifically, the service user can operate on the relevant application interface, and the operation can be a click operation, a voice operation or a timing event trigger operation, after receiving the operation, the server collects the user behavior data corresponding to the service user according to the operation, the user behavior data can be the browsing behavior data of the service user, the search behavior data of the service user, etc., at the same time, at least one commodity tag can also be determined according to the service user operation, for example, determining what the searched commodity is from the search operation of the service user, further, searching for the corresponding commodity order data according to the commodity tag, the commodity order data here is the order data corresponding to the commodity tag currently owned by the service user, including currently purchased order data of the commodity tag, and previously purchased order data of the commodity tag, etc.
[0041] Wherein, obtaining the user region data related to the service user location through device locating service where the service user is located, including data such as country, province, city, region, street, street sign, etc., user data can be composed of at least one of user behavior data, commodity order data and user region data.
[0042] Step 204, performing data calculation on the user data based on a business strategy to obtain a set of service commodities to be recommended, the set of service commodities to be recommended includes at least one service commodity to be recommended, the service commodity to be recommended is used to serve the commodity corresponding to the commodity tag.
[0043] Wherein, the preset business strategy here is used to deduce the service commodity to be recommended according to user data, service commodity is a commodity with service nature, not a physical commodity, and the commodity corresponding to the commodity tag can be a physical commodity, the corresponding service commodity can be inferred based on the physical commodity related to the service user and recommended to the service user. The preset business strategy can be set in advance according to actual business requirements, actual commodity requirements or actual application scenarios, for example, the preset business strategy can consider factors such as user behavior habits and needs of service commodity in different regions, different communities, north-south Date Recue/Date Received 2023-01-30 climate, seasons, etc., wherein, the preset business strategy can include recommendation rules corresponding to different communities, and data calculation can be performed on user data through the recommendation rules described in the preset business strategy to obtain at least one commodity to be recommended to form a commodity set to be recommended.
[0044] Wherein, performing calculation on user data by the recommendation rules described in preset business strategy, specially can be, the user data include various types of data, obtaining the recommendation rules corresponding to various types of data in the preset business data, recommending the data according to the recommendation rules, and determining the set of service commodities to be recommended from the set of candidate service commodities.
[0045] Step 206, sending the set of service commodities to be recommended to user device corresponding to service user, so that the user device displays each of service commodity to be recommended in the set of service commodities to be recommended.
[0046] Specifically, the server sends the set of service commodities to be recommended to the user device corresponding to the service user, the user device can be the user terminal, further, after the user device receives the set of service commodities to be recommended, through the application display interface, displaying each to be recommended service commodity in the to be recommended service commodity set to user, so as to achieve the recommendation effect of the service commodity.
[0047] In the above-mentioned service commodity recommendation method, obtaining user data corresponding to service user, the user data carries commodity tag, performing data calculation on the user data based on a business strategy to obtain a set of service commodities to be recommended, the set of service commodities to be recommended includes at least one service commodity to be recommended, the service commodity to be recommended is used to serve the commodity corresponding to the commodity tag, sending the set of service commodities to be recommended to user device corresponding to service user, so that the user device displays each of service commodity to be recommended in the set of service commodities to be recommended. Therefore, according to the commodity tag in the user behavior data, the service commodity that matches the commodity tag can Date Recue/Date Received 2023-01-30 be accurately recommended, the service commodity is used to serve the commodity corresponding to the commodity tag, instead of recommending the similar commodity corresponding to the commodity tag to the user in the same way, so as to meet user actual needs and improve recommendation performance.
[0048] In an embodiment, as shown in Figure 3, the user data includes commodity order data, user behavior data and user region data, the commodity order data carries commodity tag, the user behavior data carries user tag, the user region data carries user region tag, the preset business strategy includes hot sale commodity strategy, recommendation cycle strategy and region strategy.
[0049] Performing data calculation on the user data based on a business strategy to obtain a set of service commodities to be recommended, comprising:
[0050] Step 302, determining a recommendation set of hot sale service commodities corresponding to the commodity tag according to the commodity order data and the hot sale commodity strategy.
[0051] Wherein, hot sale commodity strategy is one of preset business strategies, mainly filtering out the hot sale service commodity corresponding to the commodity tag from the candidate service commodity, and the hot sale service commodity is a service commodity with high user attention rate among the service commodities. Specifically, the commodity order data in the user data and the hot sale commodity strategy in the preset business strategy are obtained, the hot sale service commodity recommendation set corresponding to the commodity tag can be found through the hot sale commodity strategy and the commodity order data. Specifically, obtaining the commodity tag corresponding to the commodity order data, searching for the hot sale service commodity with user attentions according to the commodity tag, then forming a recommendation set of hot sale service commodities.
[0052] In an embodiment, determining a recommendation set of hot sale service commodities corresponding to the commodity tag according to the commodity order data and the hot sale commodity strategy, comprising: searching for at least one matching candidate service commodity from the set of candidate service commodities according to the commodity tag; filtering out hot sale service Date Recue/Date Received 2023-01-30 commodities according to attention rate corresponding to each candidate service commodity to form a recommendation set of hot sale service commodities.
[0053] Specifically, determining commodity tag according to the commodity order data, the commodity order data includes all data related to the commodity order, therefore, extracting the commodity tag from the commodity order data, the commodity tag can be a general commodity name, commodity basic information, commodity identification, etc., then searching for at least one matching candidate service commodity from the candidate service commodity set according to the commodity tag, the mapping relationship between the candidate service commodity and the commodity tag can be established in advance, therefore, searching for candidate service commodity that matches commodity tag through the mapping relationship, and according to the attention rate corresponding to each candidate service commodity, determining hot sale service commodity, forming a recommendation set of hot sale service commodities. Wherein, the attention rate corresponding to each candidate service commodity is calculated through the attention of the candidate service commodity relative to the user, and the attention behavior can be such as searching, browsing and purchasing, etc.
[0054] For example, in the field of home appliance, the commodity order data is air conditioner order data, and the commodity tag extracted from the commodity order data is air conditioner A, searching for the matched candidate service commodity based on the air conditioner A, such as cleaning service of air conditioner A, maintenance service of air conditioner A, replacement service of air conditioner A, etc., and then determining the hot sale service commodity according to the attention rate corresponding to each candidate service commodity: cleaning service of air conditioner A and maintenance service of air conditioner A, forming a recommendation set of hot sale service commodities.
[0055] Step 304, determining a first service commodity recommendation set corresponding to the user tag according to the user behavior data and the recommendation cycle strategy.
[0056] Wherein, recommendation cycle strategy is one of preset business strategies, mainly filtering out the first service commodity that meets the recommendation cycle and corresponds to the user tag Date Recue/Date Received 2023-01-30 from the candidate service commodities, in another words, the recommendation cycle strategy is after user purchases a certain physical commodity, when the related service commodities can be recommended. Wherein, user tag can user portrait, user basic information, user identification, etc., specifically, determining commodity tag and user tag according to user behavior data, obtaining the recommendation period in the recommendation cycle strategy, according to the user behavior data and the recommendation period, obtaining at least one first service commodity matching the recommendation period and corresponding to the user tag from the candidate service commodity set, forming a recommendation set of first service commodity.
[0057] In an embodiment, determining a first service commodity recommendation set corresponding to the user tag according to the user behavior data and the recommendation cycle strategy, comprising:
obtaining a recommendation period corresponding to the recommendation cycle strategy; obtaining user usage time in the user behavior data; obtaining a recommendation set of associated first service commodities according to the recommendation period and the user usage time.
[0058] Specifically, obtaining the preset recommendation period in the recommendation cycle strategy, extracting user usage time from user behavior data, the user usage time is the time when user starts to use the purchased commodity, the usage time can be commodity receiving time, commodity installation time, etc., then, the associated first service commodity recommendation set is ob ained by filtering from the candidate service commodity set through the recommendation time and the user usage time.
[0059] For example, in the filed of home appliance, user behavior data is behavior data related to service user A, the service user A has browsed the commodity data related to air conditioner A for many times, and the service user A has purchased commodity data related to air conditioner A half a year ago, the recommendation period in recommendation cycle strategy is half a year, therefore, filtering the first service commodity that needs to be recommended to the service user A after half a year, the first service commodity is related to air conditioner A, and the first service commodity can be used to serve air conditioner A, for example, maintenance service for air conditioner A every six months.
Date Recue/Date Received 2023-01-30 [0060] Step 306, determining a second service commodity recommendation set corresponding to the region tag according to the user region data and the region strategy.
[0061] Wherein, region strategy is also one of preset business strategies, mainly fileting out the second service commodity that meets the region tag and the corresponding commodity identification from the candidate service commodities, in another words, the region strategy considers geographical factors and filtering out service commodities that meet the geographical factors of user location, for example, the recommendation of service commodities in north and south regions can be different. Specifically, obtaining user region data, filtering out the second service commodity matching the region tag according to the user region data and the rules in the region strategy.
Wherein, the region tag can be region factor, climate factor, season factor, etc., and the rules in the region strategy can be preset according to business requirements, actual commodity requirements or actual application scenarios.
[0062] In an embodiment, determining a second service commodity recommendation set corresponding to the region tag according to the user region data and the region strategy, comprising:
obtaining service region corresponding to each candidate service commodity in the candidate service commodity set; obtaining a recommendation set of second service commodities according to the filtering of the user region data and each service region.
[0063] Specifically, obtaining the corresponding service region of each candidate service commodity in the candidate service commodity set, establishing the mapping relationship between candidate service commodity and service region in advance according to actual business needs, actual commodity requirements or actual application scenarios, then obtaining the second service commodity matching the user region data according to the mapping relationship, obtaining the second service commodity set.
[0064] For example, the service region corresponding to candidate service commodity A is Guangzhou, the service region corresponding to candidate service commodity B
is Beijing, and user region data is all data of Guangzhou, therefore, the filtered second service commodity is candidate service commodity A.
Date Recue/Date Received 2023-01-30 [0065] Step 308, calculating the recommended set of hot sale service commodities, the first recommended set of service commodities and the second recommended set of service commodities according to a preset weight distribution ratio to obtain a set of service commodities to be recommended.
[0066] Wherein, the preset weight distribution ratio can be based on actual business requirements, actual commodity requirements or actual application scenarios in advance, including first weight ratio corresponding to the hot sale service commodity recommendation set, second weight ratio corresponding to the first service commodity recommendation set, and the weight ratio corresponding to the second service commodity recommendation set, then, calculating the set of service commodities to be recommended according to the corresponding weight ratio of hot sale service commodity recommendation set, the first service commodity recommendation set and the second service commodity recommendation set.
[0067] In an embodiment, calculating the recommended set of hot sale service commodities, the first recommended set of service commodities and the second recommended set of service commodities according to a preset weight distribution ratio to obtain a set of service commodities to be recommended, comprising: determining a first weight ratio, a second weight ratio and a third weight ratio according to a preset weight distribution ratio; performing sorting calculation according to the first weight ratio corresponding to the recommended set of hot sale service commodities, the second weight ratio corresponding to the first recommended set of service commodities, and the third weight ratio corresponding to the second recommended set of service commodities, obtaining a recommendation set of service commodities.
[0068] For example, region strategy recommendation occupies for 50%: optimizing the recommendation sorting according to user region, the impact of sorting is according to the business backend configuration; recommendation period occupies for 30%: based on user behavior trajectory, including: search, collection, browsing, payment, etc., sorting the recommended commodities, the weight of the same commodity increases, the different commodities are sorted and graded according to the weight; hot sale commodity strategy occupies for 20%: based on the commodity being browsed, collected, added for purchasing, and purchased, the tags are calculated, users who have no purchase Date Recue/Date Received 2023-01-30 behavior with the same tag are recommended, the weight of the same commodities increases, the different commodities are sorted and graded according to the weight, obtaining a set of service commodities to be recommended.
[0069] In a specific scenario, the service commodity is recommended for non-cleaning commodity, based on statistical calculation of user behavior trajectory data, data calculating based on user browsing, purchasing, searching and collection behaviors in Suning APP/mini-program and Suning Helper APP/mini-program within a certain period (three months, half a year, one year), performing collaborative filtering process based on user according to user behavior data to obtain a sorted set of commodities recommended to user. The collaborative filtering is divided into user based and commodity based types, during the filtering process, performing the corresponding recommendation weighting process according to user tag, for user cold boot, the commodity tag is used to make recommendations, considering and recommending according to commodities that are more popular or are more popular in the locating area, summarizing the recommendation results, summarizing and filtering the recommended commodities with low conversion rate. Summarizing processing is divided into manual and automatic types, data can be exported manually and can be summarized by the business end or information end (spss and other software can be used for data analysis), through automatic summary, tagging the commodities with low conversion rate for cold boot recommendation, sequentially performing sorting operation of recommended commodities under cold boot.
[0070] Wherein, the cold boot refers to when user does not generate behavior trajectory data, then the user past transaction records in Suning or the data stored in the form of importing from other platforms are used to calculate and recommend, if user has no transaction records and no external order information importing operation, recommending service commodities according to hot sale commodities maintained in business strategy or region strategy(based on user locating operation). If user has no service address or location address, using commodity tag to perform cold boot sorting of commodity and commodity recommendation, summarizing the recommendation results, sorting in descending order for recommended commodities with low conversion rates under cold boot.
[0071] For another example, the service commodity is recommended for cleaning commodity, based Date Recue/Date Received 2023-01-30 on statistical calculation of user behavior trajectory data, data calculating based on user browsing, purchasing, searching and collection behaviors in Suning APP/mini-program and Suning Helper APP/mini-program within a certain period (three months, half a year, one year), wherein, private domain processing of physical home appliance, decoration tool and other commodities, if confidence interval reaches required level, user-based collaborative filtering is performed according user behavior data to obtain sorted set of commodities recommended to user and provide service commodities such as land reclamation and cleaning, formaldehyde removal according to recommendation. The collaborative filtering is divided into user based and commodity based types, during the filtering process, performing the corresponding recommendation weighting process according to user tag, for user cold boot, the commodity tag is used to make recommendations, considering and recommending according to commodities that are more popular or are more popular in the locating area, summarizing the recommendation results, summarizing and filtering the recommended commodities with low conversion rate. Summarizing processing is divided into manual and automatic types, data can be exported manually and can be summarized by the business end or information end (spss and other software can be used for data analysis), through automatic summary, tagging the commodities with low conversion rate for cold boot recommendation, sequentially performing sorting operation of recommended commodities under cold boot. It should be noted that the recommendations for cleaning commodity operations are all based on cleaning commodity pool (maintenance of cleaning commodity pool by business).
[0072] What should be noted is although the steps of the above-mentioned process diagrams are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly provided instruction in this article, there is no strict order in which these steps can be performed, and they can be performed in any other orders. In addition, at least partial steps of the above-mentioned Figures can include more sub steps or multiple stages, these sub steps or stages are not necessarily completed at the same time but can be executed in different time, the execution order of these sub steps or stages is also not necessarily in sequence order but can be performed alternately with the other steps or sub steps of other steps or at least one part of the other stages.
[0073] In an embodiment, a service commodity recommendation apparatus 400, as shown in Figure Date Recue/Date Received 2023-01-30 4, comprising: obtaining module 402, calculating module 404, and sending module 406, wherein:
[0074] An obtaining module 402 configured to obtain user data corresponding to service user, the user data carries commodity tag.
[0075] A calculating module 404 configured to perform data calculation on the user data based on a business strategy to obtain a set of service commodities to be recommended, the set of service commodities to be recommended includes at least one service commodity to be recommended, the service commodity to be recommended is used to serve the commodity corresponding to the commodity tag.
[0076] A sending module 406 configured to send the set of service commodities to be recommended to user device corresponding to service user, so that the user device displays each of service commodity to be recommended in the set of service commodities to be recommended.
[0077] In an embodiment, the user data includes commodity order data, user behavior data and user region data, the commodity order data carries commodity tag, the user behavior data carries user tag, the user region data carries user region tag, the preset business strategy includes hot sale commodity strategy, recommendation cycle strategy and region strategy, calculating module 404 determines a recommendation set of hot sale service commodities corresponding to the commodity tag according to the commodity order data and the hot sale commodity strategy, determines a first service commodity recommendation set corresponding to the user tag according to the user behavior data and the recommendation cycle strategy, determines a second service commodity recommendation set corresponding to the region tag according to the user region data and the region strategy, and calculates the recommended set of hot sale service commodities, the first recommended set of service commodities and the second recommended set of service commodities according to a preset weight distribution ratio to obtain a set of service commodities to be recommended.
[0078] In an embodiment, the calculating module 404 searches for at least one matching candidate service commodity from the set of candidate service commodities according to the commodity tag, Date Recue/Date Received 2023-01-30 filtering out hot sale service commodities according to attention rate corresponding to each candidate service commodity to form a recommendation set of hot sale service commodities.
[0079] In an embodiment, the calculating module 404 obtains a recommendation period corresponding to the recommendation cycle strategy, obtains user usage time in the user behavior data, and obtains a recommendation set of associated first service commodities according to the recommendation period and the user usage time.
[0080] In an embodiment, the calculating module 404 obtains service region corresponding to each candidate service commodity in the candidate service commodity set, and obtains a recommendation set of second service commodities according to the filtering of the user region data and each service region.
[0081] In an embodiment, the calculating module 404 determines a first weight ratio, a second weight ratio and a third weight ratio according to a preset weight distribution ratio, performs sorting calculation according to the first weight ratio corresponding to the recommended set of hot sale service commodities, the second weight ratio corresponding to the first recommended set of service commodities, and the third weight ratio corresponding to the second recommended set of service commodities, and obtains a recommendation set of service commodities.
[0082] In an embodiment, the obtaining module 402 receives an operation corresponding to service user, obtains user behavior data corresponding to the service user according to the operation, obtains commodity tag according to the operation, obtaining matching commodity order data according to the commodity tag; and obtains the user region data corresponding to the service user through locating technology corresponding to the service user, forming user data according to the user behavior data, the commodity order data and the user region data.
[0083] For the specific limitation of a service commodity recommendation apparatus can refer to the above-mentioned service commodity recommendation method, which will not be repeated here. Each module in the above-mentioned service commodity recommendation apparatus can be achieved fully Date Recue/Date Received 2023-01-30 or partly by software, hardware, and their combinations. The above modules can be embedded in the processor or independent of the processor in computer device and can store in the memory of computer device in form of software, so that the processor can call and execute the operations corresponding to the above modules.
[0084] In an embodiment, a computer device is provided, the computer device can be a server whose internal structure diagram is shown in Figure 5. The computer device includes a processor, a memory, a network interface, and a database connected through a system bus. The processor of the computer device is configured to provide calculation and control capabilities. The memory of the computer device includes non-volatile storage medium and internal memory. The memory of non-volatile storage medium has an operation system, computer programs and database. The internal memory provides an environment for the operation system and computer program running in a non-volatile storage medium.
The network interface of the computer device is used to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement a service commodity recommendation method.
[0085] The skilled in the art can understand that the structure shown in Figure 5 is only partial structural diagram related this application solution and not constitute limitation to the computer device applied on the current application solution, the specific computer device can include more or less components than what is shown in the figure, or combinations of some components or different components to what is shown in the figure.
[0086] In an embodiment, a computer device is provided, including a memory, a processor and a computer program stored in the memory and ran on the processor configured to achieve the following steps when the processor executes the computer program: obtaining user data corresponding to service user, the user data carries commodity tag; performing data calculation on the user data based on a business strategy to obtain a set of service commodities to be recommended, the set of service commodities to be recommended includes at least one service commodity to be recommended, the service commodity to be recommended is used to serve the commodity corresponding to the commodity tag; sending the set of service commodities to be recommended to user device corresponding to service Date Recue/Date Received 2023-01-30 user, so that the user device displays each of service commodity to be recommended in the set of service commodities to be recommended.
[0087] In an embodiment, the user data includes commodity order data, user behavior data and user region data, the commodity order data carries commodity tag, the user behavior data carries user tag, the user region data carries user region tag, the preset business strategy includes hot sale commodity strategy, recommendation cycle strategy and region strategy, performing data calculation on the user data based on a business strategy to obtain a set of service commodities to be recommended, comprising:
determining a recommendation set of hot sale service commodities corresponding to the commodity tag according to the commodity order data and the hot sale commodity strategy;
determining a first service commodity recommendation set corresponding to the user tag according to the user behavior data and the recommendation cycle strategy; determining a second service commodity recommendation set corresponding to the region tag according to the user region data and the region strategy; calculating the recommended set of hot sale service commodities, the first recommended set of service commodities and the second recommended set of service commodities according to a preset weight distribution ratio to obtain a set of service commodities to be recommended.
[0088] In an embodiment, the processor executes computer program to achieve following steps:
searching for at least one matching candidate service commodity from the set of candidate service commodities according to the commodity tag; filtering out hot sale service commodities according to attention rate corresponding to each candidate service commodity to form a recommendation set of hot sale service commodities.
[0089] In an embodiment, the processor executes computer program to achieve following steps:
obtaining a recommendation period corresponding to the recommendation cycle strategy; obtaining user usage time in the user behavior data; obtaining a recommendation set of associated first service commodities according to the recommendation period and the user usage time.
[0090] In an embodiment, the processor executes computer program to achieve following steps:
obtaining service region corresponding to each candidate service commodity in the candidate service Date Recue/Date Received 2023-01-30 commodity set; obtaining a recommendation set of second service commodities according to the filtering of the user region data and each service region.
[0091] In an embodiment, the processor executes computer program to achieve following steps:
determining a first weight ratio, a second weight ratio and a third weight ratio according to a preset weight distribution ratio; performing sorting calculation according to the first weight ratio corresponding to the recommended set of hot sale service commodities, the second weight ratio corresponding to the first recommended set of service commodities, and the third weight ratio corresponding to the second recommended set of service commodities, obtaining a recommendation set of service commodities.
[0092] In an embodiment, the processor executes computer program to achieve following steps:
receiving an operation corresponding to service user, obtaining user behavior data corresponding to the service user according to the operation; obtaining commodity tag according to the operation, obtaining matching commodity order data according to the commodity tag; obtaining the user region data corresponding to the service user through locating technology corresponding to the service user, forming user data according to the user behavior data, the commodity order data and the user region data.
[0093] In an embodiment, a computer readable storage medium is provided, the medium stored with computer program and the processor performs the following steps when executing the computer program: obtaining user data corresponding to service user, the user data carries commodity tag;
performing data calculation on the user data based on a business strategy to obtain a set of service commodities to be recommended, the set of service commodities to be recommended includes at least one service commodity to be recommended, the service commodity to be recommended is used to serve the commodity corresponding to the commodity tag; sending the set of service commodities to be recommended to user device corresponding to service user, so that the user device displays each of service commodity to be recommended in the set of service commodities to be recommended.
[0094] In an embodiment, the user data includes commodity order data, user behavior data and user Date Recue/Date Received 2023-01-30 region data, the commodity order data carries commodity tag, the user behavior data carries user tag, the user region data carries user region tag, the preset business strategy includes hot sale commodity strategy, recommendation cycle strategy and region strategy, performing data calculation on the user data based on a business strategy to obtain a set of service commodities to be recommended, comprising:
determining a recommendation set of hot sale service commodities corresponding to the commodity tag according to the commodity order data and the hot sale commodity strategy;
determining a first service commodity recommendation set corresponding to the user tag according to the user behavior data and the recommendation cycle strategy; determining a second service commodity recommendation set corresponding to the region tag according to the user region data and the region strategy; calculating the recommended set of hot sale service commodities, the first recommended set of service commodities and the second recommended set of service commodities according to a preset weight distribution ratio to obtain a set of service commodities to be recommended.
[0095] In an embodiment, the processor executes computer program to achieve following steps:
searching for at least one matching candidate service commodity from the set of candidate service commodities according to the commodity tag; filtering out hot sale service commodities according to attention rate corresponding to each candidate service commodity to form a recommendation set of hot sale service commodities.
[0096] In an embodiment, the processor executes computer program to achieve following steps:
obtaining a recommendation period corresponding to the recommendation cycle strategy; obtaining user usage time in the user behavior data; obtaining a recommendation set of associated first service commodities according to the recommendation period and the user usage time.
[0097] In an embodiment, the processor executes computer program to achieve following steps:
obtaining service region corresponding to each candidate service commodity in the candidate service commodity set; obtaining a recommendation set of second service commodities according to the filtering of the user region data and each service region.
[0098] In an embodiment, the processor executes computer program to achieve following steps:
Date Recue/Date Received 2023-01-30 determining a first weight ratio, a second weight ratio and a third weight ratio according to a preset weight distribution ratio; performing sorting calculation according to the first weight ratio corresponding to the recommended set of hot sale service commodities, the second weight ratio corresponding to the first recommended set of service commodities, and the third weight ratio corresponding to the second recommended set of service commodities, obtaining a recommendation set of service commodities.
[0099] In an embodiment, the processor executes computer program to achieve following steps:
receiving an operation corresponding to service user, obtaining user behavior data corresponding to the service user according to the operation; obtaining commodity tag according to the operation, obtaining matching commodity order data according to the commodity tag; obtaining the user region data corresponding to the service user through locating technology corresponding to the service user, forming user data according to the user behavior data, the commodity order data and the user region data.
[0100] The skilled in the art can understand that all or partial of procedures from the above-mentioned methods can be performed by computer program instructions through related hardware, the mentioned computer program can be stored in a non-volatile material computer readable storage medium, this computer can include various embodiment procedures from the abovementioned methods when execution. Any reference to the memory, the storage, the database, or the other media used in each embodiment provided in current application can include non-volatile and/or volatile memory. Non-volatile memory can include read-only memory (ROM), programable ROM (PROM), electrically programmable ROM (EPRPMD), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an instruction but not limited to, RAM is available in many forms such as static RAM (SRAM), dynamic RAM (DRAMD), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SRAM (ESDRAM), synchronal link (Synchlink) DRAM (SLDRAM), memory bus (Rambus), direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM
(RDRAM), etc.
Date Recue/Date Received 2023-01-30 [0101] The technical features of the above-mentioned embodiments can be randomly combined, for concisely statement, not all possible combinations of technical feature in the abovementioned embodiments are described. However, if there are no conflicts in the combinations of these technical features, it shall be within the scope of this description.
[0102] The above-mentioned embodiments are only several embodiments in this disclosure and the description is more specific and detailed but cannot be understood as the limitation of the scope of the invention patent. Evidently those ordinary skilled in the art can make various modifications and variations to the disclosure without departing from the spirit and scope of the disclosure. Therefore, the appended claims are intended to be construed as encompassing the described embodiment and all the modifications and variations coming into the scope of the disclosure.
Date Recue/Date Received 2023-01-30
Claims (10)
1. A service commodity recommendation method comprises:
obtaining user data corresponding to service user, the user data carries commodity tag;
performing data calculation on the user data based on a business strategy to obtain a set of service commodities to be recommended, the set of service commodities to be recommended includes at least one service commodity to be recommended, the service commodity to be recommended is used to serve the commodity corresponding to the commodity tag;
and sending the set of service commodities to be recommended to user device corresponding to service user, so that the user device displays each of service commodity to be recommended in the set of service commodities to be recommended.
obtaining user data corresponding to service user, the user data carries commodity tag;
performing data calculation on the user data based on a business strategy to obtain a set of service commodities to be recommended, the set of service commodities to be recommended includes at least one service commodity to be recommended, the service commodity to be recommended is used to serve the commodity corresponding to the commodity tag;
and sending the set of service commodities to be recommended to user device corresponding to service user, so that the user device displays each of service commodity to be recommended in the set of service commodities to be recommended.
2. The method according to claim 1, wherein, the user data includes commodity order data, user behavior data and user region data, the commodity order data carries commodity tag, the user behavior data carries user tag, the user region data carries user region tag, the preset business strategy includes hot sale commodity strategy, recommendation cycle strategy and region strategy, performing data calculation on the user data based on a business strategy to obtain a set of service commodities to be recommended, comprising:
determining a recommendation set of hot sale service commodities corresponding to the commodity tag according to the commodity order data and the hot sale commodity strategy;
determining a first service commodity recommendation set corresponding to the user tag according to the user behavior data and the recommendation cycle strategy;
determining a second service commodity recommendation set corresponding to the region tag according to the user region data and the region strategy; and Date Recue/Date Received 2023-01-30 calculating the recommended set of hot sale service commodities, the first recommended set of service commodities and the second recommended set of service commodities according to a preset weight distribution ratio to obtain a set of service commodities to be recommended.
determining a recommendation set of hot sale service commodities corresponding to the commodity tag according to the commodity order data and the hot sale commodity strategy;
determining a first service commodity recommendation set corresponding to the user tag according to the user behavior data and the recommendation cycle strategy;
determining a second service commodity recommendation set corresponding to the region tag according to the user region data and the region strategy; and Date Recue/Date Received 2023-01-30 calculating the recommended set of hot sale service commodities, the first recommended set of service commodities and the second recommended set of service commodities according to a preset weight distribution ratio to obtain a set of service commodities to be recommended.
3. The method according to claim 2, wherein, determining a recommendation set of hot sale service commodities corresponding to the commodity tag according to the commodity order data and the hot sale commodity strategy, comprising:
searching for at least one matching candidate service commodity from the set of candidate service commodities according to the commodity tag; and filtering out hot sale service commodities according to attention rate corresponding to each candidate service commodity to form a recommendation set of hot sale service commodities.
searching for at least one matching candidate service commodity from the set of candidate service commodities according to the commodity tag; and filtering out hot sale service commodities according to attention rate corresponding to each candidate service commodity to form a recommendation set of hot sale service commodities.
4. The method according to claim 2, wherein, determining a first service commodity recommendation set corresponding to the user tag according to the user behavior data and the recommendation cycle strategy, comprising:
obtaining a recommendation period corresponding to the recommendation cycle strategy;
obtaining user usage time in the user behavior data; and obtaining a recommendation set of associated first service commodities according to the recommendation period and the user usage time.
obtaining a recommendation period corresponding to the recommendation cycle strategy;
obtaining user usage time in the user behavior data; and obtaining a recommendation set of associated first service commodities according to the recommendation period and the user usage time.
5. The method according to claim 2, wherein, determining a second service commodity recommendation set corresponding to the region tag according to the user region data and the region strategy, comprising:
obtaining service region corresponding to each candidate service commodity in the candidate Date Recue/Date Received 2023-01-30 service commodity set; and obtaining a recommendation set of second service commodities according to the filtering of the user region data and each service region.
obtaining service region corresponding to each candidate service commodity in the candidate Date Recue/Date Received 2023-01-30 service commodity set; and obtaining a recommendation set of second service commodities according to the filtering of the user region data and each service region.
6. The method according to claim 2, wherein, calculating the recommended set of hot sale service commodities, the first recommended set of service commodities and the second recommended set of service commodities according to a preset weight distribution ratio to obtain a set of service commodities to be recommended, comprising:
determining a first weight ratio, a second weight ratio and a third weight ratio according to a preset weight distribution ratio; and performing sorting calculation according to the first weight ratio corresponding to the recommended set of hot sale service commodities, the second weight ratio corresponding to the first recommended set of service commodities, and the third weight ratio corresponding to the second recommended set of service commodities, obtaining a recommendation set of service commodities.
determining a first weight ratio, a second weight ratio and a third weight ratio according to a preset weight distribution ratio; and performing sorting calculation according to the first weight ratio corresponding to the recommended set of hot sale service commodities, the second weight ratio corresponding to the first recommended set of service commodities, and the third weight ratio corresponding to the second recommended set of service commodities, obtaining a recommendation set of service commodities.
7. The method according to claim 1, wherein, obtaining user data corresponding to service user, comprising:
receiving an operation corresponding to service user, obtaining user behavior data corresponding to the service user according to the operation;
obtaining commodity tag according to the operation, obtaining matching commodity order data according to the commodity tag; and obtaining the user region data corresponding to the service user through locating technology corresponding to the service user, forming user data according to the user behavior data, the Date Recue/Date Received 2023-01-30 commodity order data and the user region data.
receiving an operation corresponding to service user, obtaining user behavior data corresponding to the service user according to the operation;
obtaining commodity tag according to the operation, obtaining matching commodity order data according to the commodity tag; and obtaining the user region data corresponding to the service user through locating technology corresponding to the service user, forming user data according to the user behavior data, the Date Recue/Date Received 2023-01-30 commodity order data and the user region data.
8. A service commodity recommendation apparatus, wherein, the apparatus comprises:
an obtaining module configured to obtain user data corresponding to service user, the user data carries commodity tag;
a calculating module configured to perform data calculation on the user data based on a business strategy to obtain a set of service commodities to be recommended, the set of service commodities to be recommended includes at least one service commodity to be recommended, the service commodity to be recommended is used to serve the commodity corresponding to the commodity tag; and a sending module configured to send the set of service commodities to be recommended to user device corresponding to service user, so that the user device displays each of service commodity to be recommended in the set of service commodities to be recommended.
an obtaining module configured to obtain user data corresponding to service user, the user data carries commodity tag;
a calculating module configured to perform data calculation on the user data based on a business strategy to obtain a set of service commodities to be recommended, the set of service commodities to be recommended includes at least one service commodity to be recommended, the service commodity to be recommended is used to serve the commodity corresponding to the commodity tag; and a sending module configured to send the set of service commodities to be recommended to user device corresponding to service user, so that the user device displays each of service commodity to be recommended in the set of service commodities to be recommended.
9. A computer device, including a memory, a processor and a computer program stored in the memory and run on the processor configured to achieve the steps of any methods in claim 1 to 7 when the processor executes the computer program.
10. A computer readable storage medium stored with a computer program configured to achieve the steps of any methods in claim 1 to 7 when the processor executes the computer program.
Date Recue/Date Received 2023-01-30
Date Recue/Date Received 2023-01-30
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