CN114581175A - Commodity pushing method and device, storage medium and electronic equipment - Google Patents
Commodity pushing method and device, storage medium and electronic equipment Download PDFInfo
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Abstract
The application relates to the technical field of communication, in particular to a commodity pushing method and device and electronic equipment, and solves the problems that in the prior art, commodity pushing is inaccurate and a user cannot actually experience commodities. Firstly, acquiring consumption behavior information, personal information and position information of a target user, then classifying the consumption behavior information and the personal information, processing and analyzing behavior characteristics, and obtaining target recommended commodity information according to an analysis result; acquiring a target store according to the position information and the target recommended commodity information; the push information is generated according to the target store address and the target recommended commodity information, the push commodity information is pushed to the target user, the push accuracy can be effectively improved, the purchase intention of various consumer groups is held, and the accurate popularization effect is achieved. Meanwhile, the target user can be helped to find a suitable store to experience the commodity on site more quickly, and the shopping experience of the user is further improved.
Description
Technical Field
The present application relates to the field of communications technologies, and in particular, to a method and an apparatus for pushing a commodity, a storage medium, and an electronic device.
Background
With the development of mobile intelligent devices such as mobile phones and tablets and the development of the internet, online shopping rapidly occupies a main channel for people to consume. The shopping websites have various commodity types and can meet different requirements of people. However, the variety of goods for online shopping is too many, and different user preferences and purchasing power are different, so that the user cannot quickly, accurately and rapidly find the needed goods suitable for himself. The existing intelligent commodity recommendation is only based on the design of big data, the big data information acquisition is only aiming at the information of commodities, and the finally recommended commodities do not meet the requirements of customers well, namely the accuracy is not high. There are problems in that the recommendation satisfaction degree and the transaction conversion rate are low.
In addition, when a user purchases goods online, the user often only knows the goods through webpage information, such as images, videos, evaluations and the like, and cannot actually experience the goods, so that the user finds that the actual goods are different from the knowledge of the user after purchasing the goods, and poor shopping experience is caused.
Disclosure of Invention
The application provides a commodity pushing method and device, a storage medium and electronic equipment, aiming at the problems that in the prior art, commodity pushing is inaccurate and a user cannot actually experience commodities.
In a first aspect, the present application provides a method for pushing a commodity, where the method includes:
acquiring consumption behavior information, personal information and position information of a target user;
analyzing the consumption behavior information and the personal information to obtain target recommended commodity information;
acquiring a target store according to the position information and the target recommended commodity information;
and generating push information according to the target store address and the target recommended commodity information.
In the above embodiment, the consumption behavior information, the personal information and the position information of the target user are obtained first, then the consumption behavior information and the personal information are classified and analyzed by behavior feature processing, and the target recommended commodity information is obtained according to the analysis result; acquiring a target store according to the position information and the target recommended commodity information; the push information is generated according to the target store address and the target recommended commodity information, the push commodity information is pushed to the target user, the push accuracy can be effectively improved, the purchase intention of various consumer groups is held, and the accurate popularization effect is achieved. Meanwhile, the target user can be helped to find a suitable store to experience the commodity on site more quickly, and the shopping experience of the user is further improved.
According to an embodiment of the present application, optionally, in the above product pushing method, the step of obtaining the consumption behavior information of the target user includes:
searching whether a target user exists in a consumption behavior information base;
and if so, acquiring the online consumption behavior information and the offline consumption behavior information of the target user.
In the above embodiment, the online consumption behavior information and the offline consumption behavior information of the target user can be quickly acquired through the consumption behavior information base, so that the efficiency and the accuracy of information acquisition are improved. In addition, the acquired consumption behavior information comprises online consumption behavior information and offline consumption behavior information, so that the consumption behavior of the target user can be comprehensively analyzed, and the commodity recommendation can be accurately carried out.
According to an embodiment of the application, optionally, in the above product pushing method, before the step of searching whether the target user exists in the consumption behavior information base, the method further includes:
acquiring offline consumption behavior information, which is acquired by intelligent equipment in all offline stores aiming at different users;
acquiring online consumption behavior information of different users;
and matching the offline consumption behavior information and the online consumption behavior information of the same user, and storing the information in a consumption behavior information base.
In the above embodiment, after the offline consumption behavior information and the online consumption behavior information are obtained, the online consumption behavior information and the offline consumption behavior of the same user are matched to ensure that the online consumption behavior information and the offline consumption behavior correspond to the same user, and then the matched offline consumption behavior information and the matched online consumption behavior information are stored in the consumption behavior information base, so that the consumption behavior information of the target user can be directly obtained from the database.
According to an embodiment of the application, optionally, in the above-mentioned product pushing method, the step of analyzing the consumption behavior information and the personal information to obtain target recommended product information includes:
and processing the consumption behavior information and the personal information through a rule recommendation system algorithm matched with the sorting to obtain target recommended commodity information.
According to an embodiment of the present application, optionally, in the above-mentioned product pushing method, the step of processing the consumption behavior information and the personal information by using a rule recommendation system algorithm of matching and sorting to obtain target recommended product information includes:
analyzing the consumption behavior information and the personal information based on a preset matching rule to obtain the preference characteristics of the target user;
sorting all the commodities to be recommended according to the preference characteristics;
and determining the information of the commodities to be recommended with the sequence number smaller than the preset threshold value after the sequencing as target recommended commodity information.
According to an embodiment of the application, optionally, in the product pushing method, the consumption behavior information includes movement information of the target user leaving an online store and an online purchase history, and the step of analyzing the consumption behavior information and the personal information based on a preset matching rule to obtain the preference characteristics of the target user includes:
determining a first preference characteristic according to the movement information;
determining a second preference characteristic from the online purchase history;
determining a preference feature according to the first preference feature and the second preference feature.
According to an embodiment of the present application, optionally, in the above commodity pushing method, the step of determining the first preference characteristic according to the movement information includes:
determining all commodities to be analyzed corresponding to the movement track in the movement information;
sequencing all the commodities to be analyzed according to the length of the stay time of the target user before each commodity to be analyzed in the mobile information;
and determining a first preference characteristic of the target user according to the sorted commodities to be analyzed.
In a second aspect, the present application further provides a merchandise pushing device, where the device includes: the information acquisition module is used for acquiring consumption behavior information, personal information and position information of a target user;
the recommended commodity determining module is used for analyzing the consumption behavior information and the personal information to obtain target recommended commodity information;
the target store obtaining module is used for obtaining a target store according to the position information and the target recommended commodity information;
and the push information generating module is used for generating push information according to the target store address and the target recommended commodity information.
According to an embodiment of the application, optionally, in the above commodity pushing device, the information obtaining module includes:
the target user searching unit is used for searching whether a target user exists in the consumption behavior information base;
and the consumption behavior information acquisition unit is used for acquiring the online consumption behavior information and the offline consumption behavior information of the target user if the online consumption behavior information and the offline consumption behavior information are acquired.
According to an embodiment of the application, optionally, in the above goods pushing device, the device further includes:
the offline consumption behavior information acquisition module is used for acquiring offline consumption behavior information acquired by intelligent equipment in all offline stores aiming at different users;
the online consumption behavior information acquisition module is used for acquiring online consumption behavior information of different users;
and the information storage module is used for matching the offline consumption behavior information and the online consumption behavior information of the same user and storing the information in the consumption behavior information base.
According to an embodiment of the present application, optionally, in the above product pushing device, the recommended product determining module includes:
and the recommended commodity determining unit is used for processing the consumption behavior information and the personal information through a rule recommending system algorithm matched with the sequence to obtain target recommended commodity information.
According to an embodiment of the application, optionally, in the above product pushing device, the recommended product determining unit includes:
the preference characteristic obtaining subunit is configured to analyze the consumption behavior information and the personal information based on a preset matching rule to obtain a preference characteristic of the target user;
the sorting subunit is used for sorting all the commodities to be recommended according to the preference characteristics;
and the target recommended commodity subunit is used for determining the information of the commodities to be recommended with the sequence number smaller than the preset threshold value after the sequencing as the target recommended commodity information.
According to an embodiment of the application, optionally, in the above product pushing apparatus, the consumption behavior information includes movement information of the target user leaving an online store and an online purchase history, and the preference feature acquiring subunit includes:
a first preference feature obtaining subunit, configured to determine a first preference feature according to the movement information;
the second preference characteristic acquisition subunit is used for determining a second preference characteristic according to the online purchase history;
and the preference characteristic determining subunit is used for determining a preference characteristic according to the first preference characteristic and the second preference characteristic.
According to an embodiment of the application, optionally, in the above article pushing device, the first preference characteristic determining subunit includes:
a to-be-analyzed commodity determining subunit, configured to determine all to-be-analyzed commodities corresponding to the movement trajectory in the movement information;
the to-be-analyzed commodity sequencing subunit is used for sequencing all the to-be-analyzed commodities according to the length of the stay time of the target user before each to-be-analyzed commodity in the mobile information;
and the first preference characteristic determining subunit is used for determining the first preference characteristic of the target user according to the sorted commodities to be analyzed.
In a third aspect, the present application provides a storage medium storing a computer program, which is executable by one or more processors and is operable to implement the method for pushing an article as described above.
In a fourth aspect, the present application provides an electronic device, including a memory and a processor, where the memory stores a computer program, and the computer program is executed by the processor to execute the above-mentioned product pushing method.
Compared with the prior art, one or more embodiments in the above scheme can have the following advantages or beneficial effects:
the application provides a commodity pushing method, a commodity pushing device, a storage medium and an electronic device, wherein the method comprises the following steps: acquiring consumption behavior information, personal information and position information of a target user; analyzing the consumption behavior information and the personal information to obtain target recommended commodity information; acquiring a target store according to the position information and the target recommended commodity information; and generating push information according to the target store address and the target recommended commodity information. Firstly, acquiring consumption behavior information, personal information and position information of a target user, then classifying the consumption behavior information and the personal information, processing and analyzing behavior characteristics, and obtaining target recommended commodity information according to an analysis result; acquiring a target store according to the position information and the target recommended commodity information; the push information is generated according to the target store address and the target recommended commodity information, the push commodity information is pushed to the target user, the push accuracy can be effectively improved, the purchase intention of various consumer groups is held, and the accurate popularization effect is achieved. Meanwhile, the target user can be helped to find a suitable store to experience the commodity on site more quickly, and the shopping experience of the user is further improved.
Drawings
The present application will be described in more detail below on the basis of embodiments and with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a commodity pushing method according to an embodiment of the present application.
Fig. 2 is a schematic block diagram of a structure of a product pushing device according to a fourth embodiment of the present application.
Fig. 3 is a connection block diagram of an electronic device according to a sixth embodiment of the present application.
In the drawings, like parts are designated with like reference numerals, and the drawings are not drawn to scale.
Detailed Description
The following detailed description will be provided with reference to the accompanying drawings and embodiments, so that how to apply the technical means to solve the technical problems and achieve the corresponding technical effects can be fully understood and implemented. The embodiments and various features in the embodiments of the present application can be combined with each other without conflict, and the formed technical solutions are all within the scope of protection of the present application.
Example one
The invention provides a commodity pushing method, please refer to fig. 1, which comprises the following steps:
step S110: and acquiring consumption behavior information, personal information and position information of the target user.
The consumption behavior information and the personal information of the target user can be obtained in advance and stored in the database. The personal information of the target user includes but is not limited to: age, gender, occupation, consumption level, etc. The location information may be obtained from the location information of the terminal device of the target user in real time, or may be a common address or a default shipping address of the target user, for example, the location information of the target user may be a living city, a street name, a residential district, or the like, or may be a current real-time location of the target user, and a specific obtaining manner may be determined according to an application scenario. The obtaining of the consumption behavior information of the target user may include online consumption behavior information and offline consumption behavior information of the target user. For example, the online consumption behavior information of the target user may include history of browsing or purchasing commodities, access amount of searching and viewing commodities, association attribute between commodities, and order information, wherein the order information includes commodity name, purchase time, commodity attribute, usage or usage, evaluation information, and association information between commodities. The online consumption behavior information can also comprise online historical purchase records, namely historical orders purchased by a target user through the Internet, and shopping preferences and shopping habits of the user can be obtained by performing big data analysis on the historical purchase records of the user, so that accurate commodity recommendation can be performed on the target user.
As an embodiment, the step S110 of obtaining the consumption behavior information of the target user includes the following steps:
step S111: searching whether a target user exists in a consumption behavior information base;
step S112: and if so, acquiring the online consumption behavior information and the offline consumption behavior information of the target user.
When the consumption behavior information of the target user is obtained, whether the target user exists or not can be searched from the consumption behavior information base, and if the target user exists, the online consumption behavior information and the offline consumption behavior information of the target user can be directly extracted from the consumption behavior information base. It is to be understood that, if the consumption behavior information of the target user does not exist in the consumption behavior information base, the consumption behavior information may be obtained in other ways. For example, the online consumption behavior information of the target user can be obtained through authorization of user online shopping software, and the offline consumption behavior information of the target user can be searched in user consumption data collected by an online store. For example, when acquiring online consumption behavior information, an ID registered by a target user may be acquired, and online consumption behavior information may be acquired using an online e-commerce system platform, for example, a store checkout counter acquires address information of a commodity or a received goods recently purchased by the target user. The online consumption behavior information of the target user, namely the online consumption behavior data of the member, comprises: the information of user's sex, age, regional location, history of browsing or purchasing commodities, logistics information, access amount of searching and viewing commodities, correlation attributes between commodities, order information include: commodity name, time of purchase, commodity attribute, use or usage method, evaluation information, and information on association between commodities. The online historical purchase record is a historical order purchased by the user through the Internet, and the shopping preference and the shopping habit of the user can be obtained by performing big data analysis on the historical purchase record of the user. For another example, the offline store may adopt the following acquisition modes for acquiring offline consumption behavior information, for example, the offline big data is acquired by using an intelligent device in the offline store, and the intelligent device may be an intelligent camera installed in the offline store. The offline consumption behavior information may include ID information of an offline store, sex/age bracket of customers who enter the store, time and frequency of arrival at the store, time of stay, route track in the store, type of goods consulted, goods purchased and history list, logistics information, order amount, and the like. And the off-line store transaction recording module is used for recording the commodity order information of successful transaction of the store-entering user, acquiring the identity information image of the target user and establishing the order transaction relationship between the identity information of the user and the purchased commodity. The merchandise information includes, but is not limited to, merchandise type, price, color, applicable time period, merchandise-associated accessories, and the like.
The online consumption behavior information and the offline consumption behavior information of the target user can be rapidly acquired through the consumption behavior information base, and the information acquisition efficiency and accuracy are improved. In addition, the acquired consumption behavior information comprises online consumption behavior information and offline consumption behavior information, so that the consumption behavior of the target user can be comprehensively analyzed, and the commodity recommendation can be accurately carried out.
As an embodiment, before the step of searching whether the target user exists in the consumption behavior information base, the consumption behavior information base can be established by the following process. The method comprises the steps of firstly, acquiring offline consumption behavior information, acquired by intelligent equipment in all offline stores aiming at different users, then acquiring online consumption behavior information of the different users, and finally matching the offline consumption behavior information and the online consumption behavior information of the same user and storing the information in a consumption behavior information base. After the offline consumption behavior information and the online consumption behavior information are obtained, the online consumption behavior information and the offline consumption behavior of the same user are matched, the online consumption behavior information and the offline consumption behavior are guaranteed to correspond to the same user, and then the matched offline consumption behavior information and the matched online consumption behavior information are stored in a consumption behavior information base, so that the consumption behavior information of a target user can be directly obtained from a database.
Step S120: and analyzing the consumption behavior information and the personal information to obtain target recommended commodity information.
When the consumption behavior information and the personal information are analyzed, the data age, the gender, the time of arriving at a store, a route of shopping, browsed commodities, a purchase list and an area of a position where the intelligent equipment of a user is positioned of a target user are mined by using information acquired by intelligent camera equipment information in an off-line store, and the target recommended commodity information is obtained after the information and the message behavior of the on-line target user are combined and processed by a matching and sequencing rule recommendation system algorithm. When the consumer behavior information and the personal information are used for matching and sorting the commodities, the commodities can be matched and sorted by using the positioned region data, the preference data of different age groups, the on-line user browsing history data, the commodity information of the history orders, the characteristic data (type, size and time) of the commodities, the activity of the commodities and the like.
Step S130: and acquiring a target store according to the position information and the target recommended commodity information.
Step S140: and generating push information according to the target store address and the target recommended commodity information.
As an implementation manner, if the data obtained by the user on-line and off-line in the processing process is the information of the position where the intelligent terminal device of the store-entering target user is located, the on-line user browsing commodity type and searching keyword data of the position area and the transaction order history data of the position area are collected by the on-line background system. The data of commodity preference of the target embrace in the area are subjected to priority ranking, secondary pushing, matching and screening of commodities are carried out according to target users at different positions, such as different age groups, different professions and the like, and commodity information pushing is accurately achieved by combining the functional characteristics of new commodities and the distance position characteristics between stores and the target users.
To sum up, the present application provides a commodity pushing method, including: acquiring consumption behavior information, personal information and position information of a target user; analyzing the consumption behavior information and the personal information to obtain target recommended commodity information; acquiring a target store according to the position information and the target recommended commodity information; and generating push information according to the target store address and the target recommended commodity information. Firstly, acquiring consumption behavior information, personal information and position information of a target user, then classifying the consumption behavior information and the personal information, processing and analyzing behavior characteristics, and obtaining target recommended commodity information according to an analysis result; acquiring a target store according to the position information and the target recommended commodity information; the push information is generated according to the target store address and the target recommended commodity information, the push commodity information is pushed to the target user, the push accuracy can be effectively improved, the purchase intention of various consumer groups is held, and the accurate popularization effect is achieved. Meanwhile, the target user can be helped to find a suitable store to experience the commodity on site more quickly, and the shopping experience of the user is further improved.
Example two
On the basis of the first embodiment, the present embodiment explains the method in the first embodiment through a specific implementation case.
In the above commodity pushing method, when the consumption behavior information and the personal information are analyzed to obtain target recommended commodity information, the consumption behavior information and the personal information may be processed by a rule recommendation system algorithm matched with the ranking to obtain the target recommended commodity information.
Optionally, the step of processing the consumption behavior information and the personal information through a rule recommendation system algorithm of matching sorting to obtain target recommended commodity information includes the following steps:
step S121: and analyzing the consumption behavior information and the personal information based on a preset matching rule to obtain the preference characteristics of the target user.
Step S122: and sequencing all the commodities to be recommended according to the preference characteristics.
The preset matching rule may include at least one of the following rules. For example, the target user may be divided according to the region of the location where the target user is located, and then sorted according to the distance data between the target user and the store with the new product. The method and the system can analyze the movement track in the store in the offline consumption behavior information of the target user to obtain the preference characteristics of the target user on the commodities, and sort all the commodities according to the preference characteristics. For the online consumption behavior information of the target user, preference characteristics can be obtained based on past purchase record analysis, and then the commodities are sorted according to the preference characteristics. The preference characteristics of different age groups can be analyzed for all the ages of the users, then the corresponding preference characteristics are determined based on the age group pair of the target user, and then the commodities are sorted according to the preference characteristics. In addition, preference characteristics can be obtained through the combination of the at least two matching rules, and the commodities to be recommended are sorted. For example, the preference characteristics of the target user are analyzed through regions, purchase records and age groups, and then the recommended commodities are matched and sorted according to the analyzed preference characteristics. And judging a new product which is closest to the store and has the top matching sequence according to the acquired position data of the target user for recommendation.
Step S123: and determining the information of the commodities to be recommended with the sequence number smaller than the preset threshold value after the sequencing as target recommended commodity information.
After all the commodities to be recommended are sorted through the preference characteristics, it can be determined that the information of the commodities to be recommended with the serial numbers smaller than the preset threshold value after sorting is target recommended commodity information. For example, the top three recommended commodities of the sorted commodities to be recommended may be used as the target recommended commodity.
It can be understood that the above mentioned goods to be recommended may be new goods or hot goods, and the specific type of the goods to be recommended may be determined according to actual situations.
As an embodiment, if the consumption behavior information includes movement information of the target user to leave the store online and an online purchase history, analyzing the consumption behavior information and the personal information based on a preset matching rule to obtain the preference of the target user includes the following processes. First, a first preference feature is determined according to the movement information, then a second preference feature is determined according to the online purchase history, and a preference feature is determined according to the first preference feature and the second preference feature.
When the first preference feature is determined according to the movement information, all the commodities to be analyzed corresponding to the movement track in the movement information may be determined first, then all the commodities to be analyzed are sorted according to the length of the stay time of the target user before each commodity to be analyzed in the movement information, and the first preference feature of the target user is determined according to the sorted commodities to be analyzed. For example, a moving track of a user is displayed, and when the user visits a physical commodity under the line, the visiting line passes through the microwave oven a, the microwave oven B, the oven C and the air conditioner D. And the residence time of the user before the microwave oven a, the microwave oven B, the oven C and the steam box D is 10 minutes, 7 minutes, 5 minutes and 1 minute, respectively, it can be determined that the first preference characteristic of the user is the kitchen appliance.
In the above embodiment, the to-be-analyzed goods which are interested by the user when the user leaves the store online can be determined according to the movement track of the user, and then the goods which are most interested by the user are determined according to the length of time that the target user stays before each to-be-analyzed goods.
It will be appreciated that where the consumption behavior information comprises a plurality of different types of data, a plurality of preference characteristics may be obtained, the number of preference characteristics in particular being determined in accordance with the analysis rules employed. For example, one preference feature may be acquired from one piece of consumption behavior information, or two or more preference features may be acquired from one piece of consumption behavior information.
In the above embodiment, the first preference feature and the second preference feature can be obtained by analyzing the movement information of the target user for getting off the store online and the online purchase history respectively, and after the two preference features are obtained, a comprehensive preference feature is determined according to the two preference features, so that the preference of the user for the commodity can be effectively determined.
EXAMPLE III
On the basis of the first embodiment, the present embodiment explains the method in the first embodiment through a specific implementation case.
For example, the new product number KT66 is a child wall-mounted air conditioner sold in 9 months in the year, the color of the air conditioner is pink, the air conditioner has the cooling and heating functions, the size of the external machine is 732, 553, 330mm, the internal machine has the automatic cleaning function, and the price is 2499 yuan. Store a001 has the object on sale, store a002 has the object on sale, store a003 has the object on sale, store a006 has the object on sale. When the offline consumption behavior information of a certain target user A is acquired, facial feature information of the user A can be extracted by using a face recognition technology from the offline acquired facial features of the user A to be matched with the facial feature information of the member information database, and if the matching is successful, the consumption behavior of the user A acquired from an offline store is determined. And then correspondingly acquiring the online consumption behavior information of the target user A from the online. If the consumption behavior information and the personal information of the target user A are female, middle year, and a girl is brought to a store, the store-entering time is 8 months, 12 days and 15:00 points, the stay time of a store-visiting route track at the position of the wall-mounted air conditioner is about 10 minutes, the mobile phone number and the addressee are provided, the wall-mounted air conditioner is arranged in the shopping cart, the order time is 10 months in the last year, the search keyword is provided with the child air conditioner, the purchase order record price is preferred to be in the range of 2000-3000, and the like. According to the matching sorting sequence rule of the push platform, the matching sorting of the target user A on the purchasing preference of the new product KT66 is obtained, the region position information of the user A is obtained based on the equipment positioning function or the mobile phone number positioning, and the A001-number store information closest to the target user A is obtained by matching and the distance from the offline store. And the processed data server is sent to the target user terminal equipment through the communication module, so that the accurate popularization effect is achieved. Target user A can obtain from the popularization information, whether the nearest online store A of leaving off from him has new KT66 to can select whether to go online store A of leaving off from the house to go true experience according to the information that obtains, reach the purpose that online accurate propelling movement can really experience the new goods to online store of leaving off the house fast.
The scheme can solve the inaccuracy of a blind pushing mode, so that the purchase intention of the target user is held, new goods can be accurately recommended for the user, physical goods which can be subjected to physical examination quickly are recommended for the target user, the target user is helped to find a suitable store with physical experience to experience the goods more quickly, and the shopping experience of the user is further improved. Various factors of the purchasing behavior of the target user are analyzed by combining offline and online user big data, the weight of the influence of the user on the purchasing behavior is judged by combining online user consuming behavior data based on the acquired device position information of the target user, and therefore commodity information with high matching degree of matching sorting data of new commodities to the user is pushed to the user through regions, purchasing records, age groups and the like, blind pushing is effectively achieved, the grasping of purchasing intentions of various consumer groups is greatly improved, and commodities are accurately recommended to the user. The method comprises the steps that age groups and purchased commodity preference information of target users are mined based on information of the target users acquired by offline intelligent equipment, matching and sequencing are carried out in combination with consumption behavior data information of the target users online, and when new product data are recommended by a new product recommending module, new product data with data matching and sequencing in the front are sent to terminal equipment of the target users by a data server through a communication module, so that an accurate popularization effect is achieved. The target user is helped to find a suitable store to experience the commodity on site more quickly, and the shopping experience of the user is further improved.
For another example, the length of time for the target user B to stay in the electric cooker area and consult is 10 minutes, and the length of time for the target user B to stay in the air conditioner is 1 minute. The sequence is the electric cooker 1 and the air conditioner 2. Then, the consumption behavior preference of the target user B on line is combined, for example, the conventional shopping order transaction evaluation unit price of the target user B is 500-1000, and 1 bit is sorted; 1000-2000 order 2 bits; color preference ordering white ordering 1 bit; black ordering 2 bits; the matching ranking of the user to a certain commodity is roughly obtained: the color preference is white, the unit price is below 1000, the electric cooker and the capacity is 5.0L. The appearance and specification of the novel electric cooker which needs to be recommended for a certain brand of goods to be recommended can be brought into pre-pushing in advance in front of the on-line and off-line matching sequence of the target user. According to purchase intention data obtained by matching the online data and the offline data, and the distance between the user and the physical store, the new item information in the nearest store of the target user B is pushed to the user, so that the user can quickly go to a nearby store with goods for experience, and the transaction rate of the user is greatly improved.
Collecting data of off-line store target users and on-line target user behavior data, classifying the collected off-line and on-line data, processing behavior data characteristics, and then screening and matching by combining user portrait data images, commodity functions and characteristic data according to the characteristic preference data of the users and the characteristics of the commodities. By extracting visual information and motion tracks of target users of the online stores and combining with previous online purchasing records, receiving addresses, search keywords and the like, the recommendation system can accurately realize pushing and synchronously optimize the goods stock of the online stores.
Example four
Referring to fig. 2, the present application provides a product pushing device 200, which includes:
an information obtaining module 210, configured to obtain consumption behavior information, personal information, and location information of a target user;
a recommended commodity determining module 220, configured to analyze the consumption behavior information and the personal information to obtain target recommended commodity information;
a target store obtaining module 230, configured to obtain a target store according to the location information and the target recommended product information;
and the push information generating module 240 is configured to generate push information according to the target store address and the target recommended commodity information.
According to an embodiment of the application, optionally, in the above commodity pushing device, the information obtaining module includes:
the target user searching unit is used for searching whether a target user exists in the consumption behavior information base;
and the consumption behavior information acquisition unit is used for acquiring the online consumption behavior information and the offline consumption behavior information of the target user if the online consumption behavior information and the offline consumption behavior information are acquired.
According to an embodiment of the present application, optionally, in the above commodity pushing device, the device further includes:
the offline consumption behavior information acquisition module is used for acquiring offline consumption behavior information acquired by intelligent equipment in all offline stores aiming at different users;
the online consumption behavior information acquisition module is used for acquiring online consumption behavior information of different users;
and the information storage module is used for matching the offline consumption behavior information and the online consumption behavior information of the same user and storing the information in the consumption behavior information base.
According to an embodiment of the present application, optionally, in the above product pushing device, the recommended product determining module includes:
and the recommended commodity determining unit is used for processing the consumption behavior information and the personal information through a rule recommending system algorithm matched with the sequence to obtain target recommended commodity information.
According to an embodiment of the application, optionally, in the above product pushing device, the recommended product determining unit includes:
the preference characteristic obtaining subunit is configured to analyze the consumption behavior information and the personal information based on a preset matching rule to obtain a preference characteristic of the target user;
the sorting subunit is used for sorting all the commodities to be recommended according to the preference characteristics;
and the target recommended commodity subunit is used for determining the information of the commodities to be recommended with the sequence number smaller than the preset threshold value after the sequencing as the target recommended commodity information.
According to an embodiment of the application, optionally, in the above product pushing apparatus, the consumption behavior information includes movement information of the target user leaving an online store and an online purchase history, and the preference feature acquiring subunit includes:
a first preference feature obtaining subunit, configured to determine a first preference feature according to the movement information;
a second preference feature obtaining subunit, configured to determine a second preference feature according to the online purchase history;
and the preference characteristic determining subunit is used for determining a preference characteristic according to the first preference characteristic and the second preference characteristic.
According to an embodiment of the application, optionally, in the above article pushing device, the first preference characteristic determining subunit includes:
a to-be-analyzed commodity determining subunit, configured to determine all to-be-analyzed commodities corresponding to the movement trajectory in the movement information;
the to-be-analyzed commodity sequencing subunit is used for sequencing all the to-be-analyzed commodities according to the length of the stay time of the target user before each to-be-analyzed commodity in the mobile information;
and the first preference characteristic determining subunit is used for determining the first preference characteristic of the target user according to the sorted commodities to be analyzed.
To sum up, the present application provides a commodity pusher, includes: an information obtaining module 210, configured to obtain consumption behavior information, personal information, and location information of a target user; a recommended commodity determining module 220, configured to analyze the consumption behavior information and the personal information to obtain target recommended commodity information; a target store obtaining module 230, configured to obtain a target store according to the location information and the target recommended product information; and the push information generating module 240 is configured to generate push information according to the target store address and the target recommended commodity information. Firstly, acquiring consumption behavior information, personal information and position information of a target user, then classifying the consumption behavior information and the personal information, processing and analyzing behavior characteristics, and obtaining target recommended commodity information according to an analysis result; acquiring a target store according to the position information and the target recommended commodity information; the push information is generated according to the target store address and the target recommended commodity information, the push commodity information is pushed to the target user, the push accuracy can be effectively improved, the purchase intention of various consumer groups is held, and the accurate popularization effect is achieved. Meanwhile, the target user can be helped to find a suitable store to experience the commodity on site more quickly, and the shopping experience of the user is further improved.
EXAMPLE five
The present embodiment also provides a computer-readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., an SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application mall, etc., where a computer program is stored, and the computer program may implement the method steps in the foregoing embodiments when executed by a processor.
EXAMPLE six
The embodiment of the application provides an electronic device, which may be a mobile phone, a computer, a tablet computer, or the like, and includes a memory and a processor, where the memory stores a computer program, and the computer program, when executed by the processor, implements the goods pushing method as described in the first embodiment. It is understood that, as shown in fig. 3, the electronic device 300 may further include: a processor 301, a memory 302, a multimedia component 303, an input/output (I/O) interface 304, and a communication component 305.
The processor 301 is configured to execute all or part of the steps in the goods pushing method according to the first embodiment. The memory 302 is used to store various types of data, which may include, for example, instructions for any application or method in the electronic device, as well as application-related data.
The Processor 301 may be implemented by an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic components, and is configured to perform the method for pushing the product according to the first embodiment.
The Memory 302 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk or optical disk.
The multimedia component 303 may include a screen, which may be a touch screen, and an audio component for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may further be stored in a memory or transmitted through a communication component. The audio assembly also includes at least one speaker for outputting audio signals.
The I/O interface 304 provides an interface between the processor 301 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons.
The communication component 305 is used for wired or wireless communication between the electronic device 300 and other devices. Wireless Communication, such as Wi-Fi, bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so that the corresponding Communication component 305 may include: Wi-Fi module, bluetooth module, NFC module.
In summary, the present application provides a method, an apparatus, a storage medium, and an electronic device for pushing a commodity, where the method includes: acquiring consumption behavior information, personal information and position information of a target user; analyzing the consumption behavior information and the personal information to obtain target recommended commodity information; acquiring a target store according to the position information and the target recommended commodity information; and generating push information according to the target store address and the target recommended commodity information. Firstly, acquiring consumption behavior information, personal information and position information of a target user, then classifying the consumption behavior information and the personal information, processing and analyzing behavior characteristics, and obtaining target recommended commodity information according to an analysis result; acquiring a target store according to the position information and the target recommended commodity information; the push information is generated according to the target store address and the target recommended commodity information, the push commodity information is pushed to the target user, the push accuracy can be effectively improved, the purchase intention of various consumer groups is held, and the accurate popularization effect is achieved. Meanwhile, the target user can be helped to find a suitable store to experience the commodity on site more quickly, and the shopping experience of the user is further improved.
In the several embodiments provided in the embodiments of the present application, it should be understood that the disclosed system and method may be implemented in other ways. The system and method embodiments described above are merely illustrative.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Although the embodiments disclosed in the present application are described above, the descriptions are only for the convenience of understanding the present application, and are not intended to limit the present application. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the disclosure as defined by the appended claims.
Claims (10)
1. A commodity pushing method is characterized by comprising the following steps:
acquiring consumption behavior information, personal information and position information of a target user;
analyzing the consumption behavior information and the personal information to obtain target recommended commodity information;
acquiring a target store according to the position information and the target recommended commodity information;
and generating push information according to the target store address and the target recommended commodity information.
2. The method of claim 1, wherein the step of obtaining consumption behavior information of the target user comprises:
searching whether a target user exists in a consumption behavior information base;
and if so, acquiring the online consumption behavior information and the offline consumption behavior information of the target user.
3. The method of claim 2, wherein prior to the step of searching the consumption behavior information base for the presence of the target user, the method further comprises:
acquiring offline consumption behavior information, which is acquired by intelligent equipment in all offline stores aiming at different users;
acquiring online consumption behavior information of different users;
and matching the offline consumption behavior information and the online consumption behavior information of the same user, and storing the information in a consumption behavior information base.
4. The method of claim 1, wherein the step of analyzing the consumption behavior information and the personal information to obtain target recommended goods information comprises:
and processing the consumption behavior information and the personal information through a rule recommendation system algorithm matched with the sorting to obtain target recommended commodity information.
5. The method of claim 4, wherein the step of processing the consumption behavior information and the personal information through a matching-ranked rule recommendation system algorithm to obtain target recommended goods information comprises:
analyzing the consumption behavior information and the personal information based on a preset matching rule to obtain preference characteristics of the target user;
sorting all the commodities to be recommended according to the preference characteristics;
and determining the information of the commodities to be recommended with the sequence numbers smaller than the preset threshold value after the sequence numbers are sequenced as target recommended commodity information.
6. The method as claimed in claim 5, wherein the consumption behavior information includes movement information of the target user for getting out of the store online and an online purchase history, and the step of analyzing the consumption behavior information and the personal information based on a preset matching rule to obtain the preference characteristics of the target user comprises:
determining a first preference characteristic according to the movement information;
determining a second preference characteristic from the online purchase history;
determining a preference feature according to the first preference feature and the second preference feature.
7. The method of claim 6, wherein the step of determining the first preferred feature according to the movement information comprises:
determining all commodities to be analyzed corresponding to the movement track in the movement information;
sequencing all the commodities to be analyzed according to the length of the stay time of the target user before each commodity to be analyzed in the mobile information;
and determining a first preference characteristic of the target user according to the sorted commodities to be analyzed.
8. An article pusher device, comprising:
the information acquisition module is used for acquiring consumption behavior information, personal information and position information of a target user;
the recommended commodity determining module is used for analyzing the consumption behavior information and the personal information to obtain target recommended commodity information;
the target store obtaining module is used for obtaining a target store according to the position information and the target recommended commodity information;
and the push information generating module is used for generating push information according to the target store address and the target recommended commodity information.
9. A storage medium storing a computer program which, when executed by one or more processors, is adapted to carry out the method of any one of claims 1 to 7.
10. An electronic device, comprising a memory and a processor, the memory having stored thereon a computer program which, when executed by the processor, performs the method of any one of claims 1-7.
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| CN202210127605.1A CN114581175A (en) | 2022-02-11 | 2022-02-11 | Commodity pushing method and device, storage medium and electronic equipment |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115796926A (en) * | 2023-02-09 | 2023-03-14 | 北京装库创意科技有限公司 | Customer obtaining method and system based on user browsing preference |
| CN116228342A (en) * | 2022-10-24 | 2023-06-06 | 广州麦乐数字科技有限公司 | Commodity recommendation method and device and computer readable storage medium |
| CN116823354A (en) * | 2023-06-08 | 2023-09-29 | 湖南华创科技发展有限公司 | Store marketing pushing method and device based on big data and storage medium |
| CN116823354B (en) * | 2023-06-08 | 2024-05-31 | 湖南华创科技发展有限公司 | Store marketing pushing method and device based on big data and storage medium |
-
2022
- 2022-02-11 CN CN202210127605.1A patent/CN114581175A/en active Pending
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116228342A (en) * | 2022-10-24 | 2023-06-06 | 广州麦乐数字科技有限公司 | Commodity recommendation method and device and computer readable storage medium |
| CN116228342B (en) * | 2022-10-24 | 2024-02-06 | 广州麦乐数字科技有限公司 | Commodity recommendation method and device and computer readable storage medium |
| CN115796926A (en) * | 2023-02-09 | 2023-03-14 | 北京装库创意科技有限公司 | Customer obtaining method and system based on user browsing preference |
| CN116823354A (en) * | 2023-06-08 | 2023-09-29 | 湖南华创科技发展有限公司 | Store marketing pushing method and device based on big data and storage medium |
| CN116823354B (en) * | 2023-06-08 | 2024-05-31 | 湖南华创科技发展有限公司 | Store marketing pushing method and device based on big data and storage medium |
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