WO2020140399A1 - 基于用户行为的产品推荐方法、装置、设备及存储介质 - Google Patents

基于用户行为的产品推荐方法、装置、设备及存储介质 Download PDF

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WO2020140399A1
WO2020140399A1 PCT/CN2019/092100 CN2019092100W WO2020140399A1 WO 2020140399 A1 WO2020140399 A1 WO 2020140399A1 CN 2019092100 W CN2019092100 W CN 2019092100W WO 2020140399 A1 WO2020140399 A1 WO 2020140399A1
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product
behavior
historical
recommended
user
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PCT/CN2019/092100
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English (en)
French (fr)
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石志娟
黄燕霞
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平安科技（深圳）有限公司
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Publication of WO2020140399A1 publication Critical patent/WO2020140399A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0631—Item recommendations

Definitions

  • This application relates to the field of big data analysis, and in particular, to a product recommendation method, device, device, and storage medium based on user behavior.
  • the traditional product recommendation method is to place a large number of product advertisements on Internet websites, or recommend the same current main product on the homepage recommendation position of some product platforms, that is, recommend the same product to different users.
  • the effect of this recommendation method is very unsatisfactory; at the same time, useless products Pushing will also consume a lot of energy and time from the user, and may even arouse the user's resentment, thereby affecting the promotion of the product.
  • the main purpose of the present application is to provide a product recommendation method, device, equipment and storage medium based on user behavior, aiming to implement targeted product recommendation and improve the recommendation effect.
  • the present application provides a product recommendation method based on user behavior.
  • the product recommendation method based on user behavior includes:
  • the present application also provides a device for recommending products based on user behavior.
  • the device for recommending products based on user behavior includes:
  • the data acquisition module is used to acquire historical behavior data of the user to be recommended, and determine to determine the corresponding historical product set according to the historical behavior data, the historical behavior data includes the behavior type and behavior of the user to be recommended to the historical product Time of occurrence;
  • the interest determination module is used to calculate the real interest score of each historical product in the historical product set according to the behavior type and behavior occurrence time of the historical behavior data, and determine the pending interest in the historical product set according to the real interest score Recommend users' real interest products;
  • the information pushing module is used to obtain recommended product information of recommended products having an association relationship with the real interest product, and push the recommended product information to a user terminal corresponding to the user to be recommended based on a preset recommendation rule.
  • the present application also provides a product recommendation device based on user behavior
  • the product recommendation device based on user behavior includes a processor, a memory, and is stored on the memory and can be used by the processor Executed computer-readable instructions, where the computer-readable instructions are executed by the processor to implement the steps of the product recommendation method based on user behavior as described above.
  • the present application also provides a storage medium that stores computer readable instructions, where the computer readable instructions are executed by a processor to implement a user behavior-based product as described above Recommended method steps.
  • This application analyzes the user's real interest based on the historical behavior data of the user, and then obtains and pushes the recommended products associated with the product based on the real interest, so that the product recommendation results meet the actual needs of the user, thereby improving the recommendation effect; at the same time, analyzing the user's In real interest, based on the two dimensions of user behavior type and behavior time, to a certain extent, it can reduce user interest analysis caused by non-real interest behavior data (noise) caused by user unconscious behavior browsing, advertising, marketing activities and other factors.
  • the adverse effects also simulate the user's interest attenuation through time decay, which further improves the accuracy of interest analysis.
  • FIG. 1 is a schematic diagram of a hardware structure of a product recommendation device based on user behavior involved in an embodiment of the present application
  • FIG. 2 is a schematic flowchart of a first embodiment of a product recommendation method based on user behavior in this application;
  • FIG. 3 is a schematic flowchart of a second embodiment of a product recommendation method based on user behavior in this application;
  • FIG. 4 is a schematic diagram of functional modules of a first embodiment of a product recommendation device based on user behavior in this application.
  • the product recommendation method based on user behavior involved in the embodiments of the present application is mainly applied to a product recommendation device based on user behavior.
  • the product recommendation device may be implemented by a device with a data processing function such as a personal computer (PC), a server, or the like.
  • FIG. 1 is a schematic diagram of a hardware structure of a product recommendation device based on user behavior involved in an embodiment of the present application.
  • the product recommendation device may include a processor 1001 (for example, a central processing unit, CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005.
  • the communication bus 1002 is used to realize the connection and communication between these components;
  • the user interface 1003 may include a display (Display), an input unit such as a keyboard (Keyboard);
  • the network interface 1004 may optionally include a standard wired interface, a wireless interface (Such as wireless fidelity WIreless-FIdelity, WI-FI interface);
  • the memory 1005 can be a high-speed random access memory (random access memory, RAM), or a stable memory (non-volatile memory), such as disk memory, memory 1005 may optionally be a storage device independent of the foregoing processor 1001.
  • RAM random access memory
  • non-volatile memory such as disk memory
  • memory 1005 may optionally be a storage device independent of the foregoing processor 1001.
  • FIG. 1 does not constitute a limitation on the present application, and may include more or less components than those illustrated, or combine certain components, or arrange different components.
  • the memory 1005 as a computer-readable storage medium in FIG. 1 may include an operating system, a network communication module, and computer-readable instructions.
  • the network communication module is mainly used to connect to a database and perform data communication with the database; and the processor 1001 can call computer-readable instructions stored in the memory 1005 and perform product recommendation based on user behavior provided by embodiments of the present application method.
  • the embodiments of the present application provide a product recommendation method based on user behavior.
  • FIG. 2 is a schematic flowchart of a first embodiment of a product recommendation method based on user behavior of the present application.
  • the product recommendation method based on user behavior includes the following steps:
  • Step S10 Obtain the historical behavior data of the user to be recommended, and determine to determine the corresponding historical product set according to the historical behavior data.
  • the historical behavior data includes the behavior type and behavior occurrence time of the historical product of the user to be recommended;
  • the traditional product recommendation method is to place a large number of product advertisements on Internet websites, or recommend the same current main product on the homepage recommendation position of some product platforms, that is, recommend the same product to different users.
  • the effect of this recommendation method is very unsatisfactory; at the same time, useless products Pushing will also consume a lot of energy and time from the user, and may even arouse the user's resentment, thereby affecting the promotion of the product.
  • this embodiment proposes a product recommendation method based on user behavior, analyzes the user's real interest based on the user's historical behavior data, and then obtains and pushes the recommended product associated with the product based on the real interest product, so that the product recommendation result Meet the actual needs of users, thereby improving the recommendation effect.
  • the product recommendation method based on user behavior in this embodiment is implemented by a product recommendation device based on user behavior, which uses a recommendation server as an example for description; and for the product, it may be financial products such as stocks, funds, and insurance. It can also be other digital products, daily necessities, etc. In this embodiment, a financial product is used as an example for description.
  • the recommendation server In order to implement targeted product recommendation, the recommendation server first needs to obtain the historical behavior data of the user to be recommended.
  • Historical behavior data are the historical operation behavior of the user to be recommended on some products, and their behavior types include but are not limited to browsing, searching, and clicking Products, collections (following), sharing, purchasing, etc.; at the same time, the historical behavior data also includes the occurrence time of each historical behavior, and the behavior object products of each historical behavior, and the collection of behavior object products of each historical behavior can be called Historical product set; for example, the user to be recommended browsed three financial products A, B, and C on x, y, and z, 2018, of which B was browsed by searching, and purchased product B, followed product C, of which The historical product set includes A, B, and C financial products.
  • the statistical software development kit SDK (Software Development Kit) can be embedded in the terminal application app in advance, and when the user to be recommended installs the terminal application app on their own user terminal, the SDK requests Obtain the operation behavior information of the user to be recommended; when the user to be recommended agrees, the user terminal will record the user's search, browsing and other behaviors according to the internal statistical logic of the SDK, combined with the identity information of the user to be recommended (such as terminal IP address, user Account, etc.) Generate corresponding historical behavior data, and then send the historical behavior data to the recommendation server.
  • the recommendation server can obtain the historical behavior data of the user to be recommended through the statistical SDK installed in the application of the user terminal.
  • the buried point logic on the script of the relevant product website for statistics.
  • the website server of the website will obtain the current visit. Identify the user terminal's identity information and record its behavior to generate historical behavior data, etc., and send the historical behavior data to the recommendation server; and the relevant statistical conditions of the buried point logic can be set according to the actual situation, for example, It means that the page stays longer than the preset time, performs specific operations on a specific element of the page (such as clicks, favorites, etc.), and uses specific keywords for retrieval.
  • the historical behavior data of the user to be recommended can also be collected by a third-party organization, and the recommendation server obtains these historical behavior data from the third-party organization.
  • Step S20 Calculate the real interest score of each historical product in the historical product set according to the behavior type and behavior occurrence time of the historical behavior data, and determine the user’s recommendation in the historical product set according to the real interest score Products of real interest;
  • the historical behavior of the user to be recommended can reflect the interests of the user to be recommended to a certain extent, so when the recommendation server obtains historical behavior data and historical product sets, it will analyze the historical behavior data to determine the user Products of interest.
  • the historical behaviors of the users to be recommended are not necessarily operations performed by the users to be recommended driven by their own interests (or needs). It is possible that the actions of the users to be recommended are influenced by other factors; For example, the user to be recommended enters the browsing interface of financial products from other event promotion windows, and this historical behavior is an unconscious behavior; for example, the user to be recommended to retrieve a certain financial is not a purchase, but to search for a friend; These historical behaviors must not fully reflect the true interests of users.
  • the users to be recommended have purchased product B, followed product C, and viewed product A (no other clicks on product A, Special operations such as attention).
  • the user's interest in the three products A, B, and C to be recommended can be considered different, and they are most interested in product B, followed by product C and product A, respectively.
  • the occurrence time of historical behavior (or the time of data collection) also has a certain timeliness to reflect the user's interest.
  • the behavior type and the time of occurrence of the comprehensive historical behavior data will be analyzed, and the two dimensions of the behavior type and the time of occurrence will be analyzed.
  • the recommended user’s historical behavior data is quantified into corresponding values, and the value is calculated according to a certain algorithm to obtain the real interest score of each historical product that the user to be recommended has been involved in, to characterize the user to be recommended to these historical products Degree of interest (or likelihood of interest), and further determine the real interest product of the user to be recommended based on the real rating; where the higher the real interest rating of a certain history, the more likely the user to be recommended is the historical product Interest (or the higher the likelihood that the user to be recommended is interested in the historical product).
  • each piece of historical behavior data it includes the behavior type and time of occurrence of the corresponding historical behavior, and of course includes objects of historical behavior (involved financial products). These objects of historical behavior form a historical product set.
  • each historical behavior of the user to be recommended will give the product a real interest score bonus; the more historical behaviors for a product, the higher the real interest score of the product High; for example, if the user to be recommended browses product A 2 times and product B 5 times, the real interest score of product B is higher than that of product A.
  • there is a difference in the degree of bonus to the true interest score of the product For example, the user to be recommended browses product A once and purchases product B once.
  • the degree of interest reflected by the behavior is higher than that of browsing behavior, so the real interest score of product A is higher than that of product B.
  • a decay function can be set to characterize the decay of the real interest score addition generated by the historical behavior (that is, the decay of the interest of the user to be recommended for a certain historical product).
  • the recommendation server can first calculate the behavior types and behavior times of the users to be recommended for each historical product, and then quantify the behavior behavior types and behavior times into corresponding values, and perform attenuation processing on the values according to the time when the behavior occurs.
  • the real interest score of each historical product is obtained.
  • the step of calculating the real interest score of each historical product in the historical product set according to the behavior type and behavior occurrence time of the historical behavior data includes:
  • the recommendation server may first classify its behavior objects (that is, historical products corresponding to the behavior) to obtain various types of product behavior data corresponding to each historical product.
  • the product behavior data of product A includes: product A was browsed at z1 on January 1, 2017, and product A was purchased at z2 on January 2, 2017; and product behavior of product B was 2017 x1, y1 I browsed product B on the day of z1, and browsed product B on the day of x1, y3, 2017.
  • the behavior data of each historical product of the user to be recommended can be obtained.
  • the recommendation server When obtaining product behavior data, the recommendation server will perform statistical analysis on each product data, and determine the behavior times of each behavior type in each product behavior data, and determine the latest behavior time of each behavior type (the last time this behavior occurred time). For example, for the product behavior data of product A, including M browsing behaviors and N purchase behaviors, the most recent behavior time of browsing behaviors for product A is 2017x1yyyzz1. The most recent behavior time is 2017 x1yyz z2 o'clock.
  • the behavior times and behavior time of each behavior type corresponding to each historical product are substituted into the preset interest score formula to calculate the real interest score of each historical product.
  • the behavior times and the latest behavior time can be substituted into a preset interest score formula to calculate each history The product's true interest score.
  • different behavior types have different interest score bonuses; the more behaviors of the same behavior type, the higher the interest score bonus of the behavior type; at the same time, each behavior The type of interest score will be affected by time decay.
  • the preset interest score formula can be:
  • P is the real interest score of each historical product
  • n is the type of behavior type included in the product behavior data, n ⁇ 1;
  • Q i is the preset score value corresponding to the i-th historical behavior in the product behavior data, Q i ⁇ 0, i ⁇ 1;
  • K i is the behavior number of the i-th historical behavior in the product behavior data, K i ⁇ 0;
  • the real interest score corresponding to each historical product in the historical product set is calculated, several historical products with the highest real interest score can be determined as the real interest products of the user to be recommended (of course for the number of real interest products It can be defined according to the actual situation); or a historical product with a real interest score higher than a preset interest threshold as a real interest product (when there is no historical product with a real interest score higher than a preset interest threshold, A historical product can be randomly selected as a real interest product, or a historical product with the highest real interest score can be used as a real interest product).
  • the real interest products of that type can also be determined separately.
  • the financial history products of the user to be recommended include stocks and fund products, then the real interest products of the stock category can be determined separately. And real interest products in the fund category.
  • Step S30 Obtain recommended product information of a recommended product having an association relationship with the real interest product, and push the recommended product information to a user terminal corresponding to the user to be recommended based on a preset recommendation rule.
  • the recommendation server when the recommendation server determines the real interest product of the user to be recommended, it can perform a targeted product query according to the real interest product, obtain the recommended product having an association relationship with the real interest product, and obtain the recommended product Recommend product information.
  • the association relationship may be embodied in different types of real interest products. For example, for financial products such as stocks, funds, and insurance, the relationship can be similar in amount range, operating organization, and risk level. For digital products, the relationship can be the same function, similar price, and the same brand.
  • the recommendation server when the recommendation server obtains the recommended product information of the recommended product, it can push the recommended product information to the user terminal of the recommended user according to a preset recommendation rule.
  • the preset recommendation rule may include provisions such as recommendation time, pushing frequency, and pushing data amount.
  • the step of obtaining recommended product information of a recommended product having an association relationship with the real interest product includes:
  • the recommendation server may first determine the fund risk type of the real interest product (fund), such as conservative, robust, aggressive, etc., among which different fund risk types Corresponding to different risk levels. At the same time, the recommendation server will also obtain the stock holding information of the real interest product (fund), the stock holding information includes the stock name, the industry of each stock issuer, the market value of each stock holding, etc.; then the stock holding information Determine the heavy stocks of the product of real interest, where the heavy stocks are the highest stocks in the market. When determining the heavy stock, the recommendation server will also determine the industry type of the heavy stock (ie, the industry to which the stock issuer belongs).
  • the recommendation server when determining the industry type of heavy stocks, the recommendation server will query the optional stocks of the industry type and obtain the stock price change information of these optional stocks within a preset period, and then determine these based on these stock price change information
  • Optional stock type of stock risk type For example, taking "one week" as a cycle, if a stock's stock price extreme value fluctuation range is less than 5%, then the stock's stock risk type is conservative; if the stock's stock price extreme value fluctuation range is 5% to 10% In between, the stock risk type of the stock is robust; if the extreme value fluctuation range of a stock is greater than 10%, the stock risk type of the stock is aggressive.
  • an optional stock having the same (or similar) risk level as the real interest product may be determined according to the stock risk type and fund risk type, for example, both are conservative Type, the same is robust type, etc.; the optional stock is the recommended stock associated with the real interest product, and the recommended stock is determined as the recommended product.
  • the recommendation server when determining the recommended product, can obtain the recommended product information of the recommended product for pushing to the user to be recommended.
  • the above determines the heavy stocks from the interest funds of the users to be recommended, and then selects the recommended stocks from the industry of the heavy stocks, so that the recommended products are similar to the interest products in the industry field, and it is also beneficial to reduce the operating agencies of interest funds.
  • the adverse impact of daily operation operations on recommended stocks; and when selecting recommended stocks, the risk tolerance level of users will also be considered to improve the fit of recommended products to users' interests.
  • the step of obtaining recommended product information of the recommended product having an association relationship with the real interest product includes:
  • the recommendation server may first determine the holder of the stock, where the holding of the stock may include fund institutions, individuals, companies, etc.; then the recommendation server may Among the holders, the highest fund holding the stock with the highest amount is determined, that is, the fund with the largest share of the stock, and the operating institution of the highest fund is determined.
  • the recommendation server When determining the operating agency of the highest fund of the stock, the recommendation server will query all optional (purchasable) funds operated by the operating agency, and use these optional funds as recommended products.
  • the recommendation server when determining the recommended product, can obtain the recommended product information of the recommended product for pushing to the user to be recommended.
  • the above recommends the fund products of fund operating institutions that hold high amounts of stocks of interest, from the perspective of the operator to let users understand other fund products related to the stock, so that users can easily obtain the products they need and improve the recommendation effect.
  • the browsing habits of the user to be recommended can also be analyzed based on historical behavior data, and then targeted product recommendations can be made according to the user's browsing habits.
  • the recommendation server may analyze the historical behavior data of the recommended user to obtain the high-frequency browsing period of the user to be recommended; for example, the user to be recommended has 5 days in the past 7 days at 12 noon to 12:30, Browsing the product from 22 pm to 22:20 pm, the high frequency browsing period of the target user can be considered as 12 pm to 12:30 pm and 22 pm to 22:20 pm.
  • the recommendation server will also determine the period duration of each high-frequency browsing period.
  • the recommendation server When detecting that the current time is in the high-frequency browsing period, the recommendation server will determine the amount of recommended information according to the period of the current high-frequency browsing period; where the relationship between the recommended information amount and the period of time may be a preset rule Set it up, for example, 10 minutes corresponds to 1 product, 20 minutes corresponds to 3 products, etc.
  • the recommended information can also be characterized according to the type of product information, for example, 10 minutes corresponds to the product name and introduction, The 20-minute duration corresponds to the detailed introduction of the product.
  • the recommendation server may push the corresponding recommended product information to the user terminal of the user to be recommended according to the recommended information amount. In the above manner, the time and amount of information recommended by the product can be closer to the browsing habits of the user to be recommended, reducing the situation of user annoyance caused by invalid push, which is beneficial to improving the recommendation effect.
  • the historical behavior data includes the behavior type and behavior of the user to be recommended to the historical product Occurrence time; calculate the real interest score of each historical product in the historical product set according to the behavior type and behavior occurrence time of the historical behavior data, and determine the user’s recommendation in the historical product set according to the real interest score Real interest products; obtaining recommended product information of recommended products having an associated relationship with the real interest products, and pushing the recommended product information to a user terminal corresponding to the user to be recommended based on preset recommendation rules.
  • this embodiment analyzes the user's real interest based on the historical behavior data of the user, and then obtains and pushes the recommended products associated with the product based on the real interest, so that the product recommendation results meet the actual needs of the user, thereby improving the recommendation effect; at the same time .
  • analyzing the user's real interest based on the user's behavior type and behavior time two dimensions, which can reduce the user's unconscious behavior browsing, advertising, marketing activities and other factors caused by non-real interest behavior data (noise)
  • the adverse impact caused by user interest analysis also simulates the user's interest attenuation through time decay, which further improves the accuracy of interest analysis.
  • FIG. 3 is a schematic flowchart of a second embodiment of a product recommendation method based on user behavior in this application.
  • the recommended product information includes a manual service link, and after step S30, it also includes:
  • Step S40 When receiving the manual service request sent by the user terminal based on the manual service link, query the corresponding manual customer service terminal according to the recommended product information, and send the corresponding service task information to the manual customer service terminal.
  • manual consultation service may also be provided for the user to be recommended.
  • the recommended product information pushed by the recommendation server includes a manual service link; after the recommended user browses the pushed recommended product information through the user terminal, if he needs to consult with the customer service staff manually, he can click the user terminal
  • the manual service link triggers the corresponding manual service request; the user terminal sends the manual service request to the recommendation server according to the operation of the user to be recommended.
  • the recommendation server When the recommendation server receives the manual service request, it will first query the corresponding manual customer service terminal (the terminal of the business person responsible for the credit product, product manager, etc.) according to the recommended product information, and send the corresponding to the manual customer service terminal Service task information; where the service task information can include the IP address, account name, phone number, etc. of the user terminal, so that the customer service staff can contact the user to be recommended through the manual customer service terminal, provide manual service for the target user, and improve the target User service experience.
  • the service task information can include the IP address, account name, phone number, etc. of the user terminal, so that the customer service staff can contact the user to be recommended through the manual customer service terminal, provide manual service for the target user, and improve the target User service experience.
  • the embodiments of the present application also provide a product recommendation device based on user behavior.
  • FIG. 4 is a schematic diagram of function modules of a first embodiment of a product recommendation device based on user behavior in this application.
  • the product recommendation device based on user behavior includes:
  • the data acquisition module 10 is used to acquire historical behavior data of the user to be recommended, and determine to determine the corresponding historical product set according to the historical behavior data.
  • the historical behavior data includes the behavior type and historical product behavior of the user to be recommended Time when the behavior occurred;
  • the interest determination module 20 is configured to calculate the real interest score of each historical product in the historical product set according to the behavior type and behavior occurrence time of the historical behavior data, and determine the real interest score in the historical product set according to the real interest score The products of real interest of the user to be recommended;
  • the information pushing module 30 is configured to obtain recommended product information of recommended products having an association relationship with the real interest product, and push the recommended product information to a user terminal corresponding to the user to be recommended based on a preset recommendation rule.
  • each virtual function module of the above-mentioned user behavior-based product recommendation device is stored in the memory 1005 of the user behavior-based product recommendation device shown in FIG. 1 and is used to implement all functions of computer-readable instructions; each module is used by the processor 1001 During execution, it is possible to obtain the user's historical behavior data, analyze the user's interest products from these historical behavior data, and perform related product push functions according to the interest product.
  • the interest determination module 20 includes:
  • a data classification unit configured to classify the historical behavior data according to the historical products corresponding to the historical behavior data to obtain the product behavior data corresponding to each historical product;
  • the data statistics unit is used to separately count the behavior times of each behavior type in the product behavior data, and determine the latest behavior time of each behavior type;
  • the score calculation unit is used to substitute the behavior times of each behavior type corresponding to each historical product and the latest behavior time of each behavior type into a preset interest score formula to calculate the real interest score of each historical product value.
  • the preset interest score formula is:
  • P is the actual interest score of each historical product
  • n is the type of behavior type included in the product behavior data, n ⁇ 1;
  • Q i is the preset score addition corresponding to the i-th historical behavior in the product behavior data, Q i ⁇ 0, i ⁇ 1;
  • K i is the behavior number of the i-th historical behavior in the product behavior data, K i ⁇ 0;
  • F(t i -t 0 ) is the time decay function
  • t i is the latest behavior time of the i-th historical behavior in the product behavior data
  • t 0 is the current time
  • the product type of the real interest product is a fund
  • the interest determination module 20 includes:
  • the first determining unit determines the fund risk type of the real interest product and the heavy stocks held by the real interest product, and determines the industry type of the heavy stocks;
  • the second determining unit is used to query the optional stocks corresponding to the industry type, and determine the stock risk type of the optional stocks according to the stock price changes of the optional stocks in a preset period;
  • a third determining unit configured to determine recommended stocks associated with the real interest product in the selectable stocks according to the stock risk type and fund risk type, and determine the recommended stocks as recommended products;
  • the first obtaining unit is configured to obtain recommended product information of the recommended product.
  • the product type of the real interest product is stock
  • the interest determination module 20 includes:
  • the fourth determination unit is used to determine the highest amount of funds holding the real interest product and determine the operating institution of the highest amount of funds
  • the fifth determining unit is used to query the optional funds operated by the operating agency and determine the optional funds as recommended products;
  • the second obtaining unit is configured to obtain recommended product information of the recommended product.
  • the information pushing module 30 includes:
  • a period acquisition unit configured to acquire a high-frequency browsing period of the user to be recommended according to the historical behavior data, and determine the period duration of the high-frequency browsing period
  • the information pushing unit is used to determine the amount of recommended information according to the time period of the current high-frequency browsing period when the current time is in the high-frequency browsing period, and to the user terminal of the user to be recommended according to the recommended information amount Push the recommended product information.
  • the recommended product information includes a manual service link
  • the product recommendation device based on user behavior further includes:
  • the task sending module is configured to, when receiving a manual service request sent by the user terminal based on the manual service link, query the corresponding manual customer service terminal according to the recommended product information, and send the corresponding service to the manual customer service terminal Task information.
  • each module in the device for recommending products based on user behavior corresponds to the steps in the embodiment of the method for recommending products based on user behavior, and the functions and implementation processes thereof will not be described here one by one.
  • an embodiment of the present application further provides a storage medium, and the storage medium may be a non-volatile readable storage medium.
  • the storage medium of the present application stores computer-readable instructions, where the computer-readable instructions are executed by the processor to implement the steps of the product recommendation method based on user behavior as described above.
  • the methods in the above embodiments can be implemented by means of software plus the necessary general hardware platform, and of course, can also be implemented by hardware, but in many cases the former is better Implementation.
  • the technical solution of the present application can be embodied in the form of a software product in essence or part that contributes to the prior art, and the computer software product is stored in a storage medium (such as ROM/RAM) as described above , Disk, CD), including several instructions to make a terminal device (may be a mobile phone, computer, server, air conditioner, or network equipment, etc.) to perform the methods described in various embodiments of the present application.

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Abstract

本申请涉及大数据分析领域，提供一种基于用户行为的产品推荐方法、装置、设备及存储介质，该方法包括：获取待推荐用户的历史行为数据，并确定根据所述历史行为数据确定所对应的历史产品集；根据所述历史行为数据的行为类型和行为发生时间计算所述历史产品集中各历史产品的真实兴趣评分，并根据所述真实兴趣评分在所述历史产品集中确定所述待推荐用户的真实兴趣产品；获取与所述真实兴趣产品具有关联关系的推荐产品的推荐产品信息，并基于预设推荐规则向所述待推荐用户对应的用户终端推送所述推荐产品信息。本申请可获取用户历史行为数据，从这些历史行为数据中分析用户的兴趣产品，并根据该兴趣产品进行关联产品推送，提升了产品推荐的效果。

Description

基于用户行为的产品推荐方法、装置、设备及存储介质
本申请要求于2019年1月4日提交中国专利局、申请号为201910014579.X、发明名称为“基于用户行为的产品推荐方法、装置、设备及存储介质”的中国专利申请的优先权，其全部内容通过引用结合在申请中。
技术领域
本申请涉及大数据分析领域，尤其涉及一种基于用户行为的产品推荐方法、装置、设备及存储介质。
背景技术
随着网络技术的发展，网络成为用户获取产品的主要平台之一。如何有效的向网络用户推荐产品，是产品提供商重点关注的问题。
传统的产品推荐方法，是通过在互联网网站上大量投放产品广告，或是在某些产品平台的首页推荐位置推荐相同的当前主推产品，即向不同的用户推荐相同的产品。然而这种“广撒网”式宣传行为，由于针对对象以及推荐的产品不明确，且不同的用户对同一产品的关注度不同，导致这种推荐方法的效果很不理想；同时，无用的产品推送还会耗费用户比较多的精力和时间，甚至于还会引起用户的反感，从而影响了产品的推广。
发明内容
本申请的主要目的在于提供一种基于用户行为的产品推荐方法、装置、设备及存储介质，旨在实现针对性的进行产品推荐，提升推荐效果。
为实现上述目的，本申请提供一种基于用户行为的产品推荐方法，所述基于用户行为的产品推荐方法包括：
获取待推荐用户的历史行为数据，并确定根据所述历史行为数据确定所对应的历史产品集，所述历史行为数据包括所述待推荐用户对历史产品的行为类型和行为发生时间；
根据所述历史行为数据的行为类型和行为发生时间计算所述历史产品集中各历史产品的真实兴趣评分，并根据所述真实兴趣评分在所述历史产品集中确定所述待推荐用户的真实兴趣产品；
获取与所述真实兴趣产品具有关联关系的推荐产品的推荐产品信息，并基于预设推荐规则向所述待推荐用户对应的用户终端推送所述推荐产品信息。
此外，为实现上述目的，本申请还提供一种基于用户行为的产品推荐装置，所述基于用户行为的产品推荐装置包括：
数据获取模块，用于获取待推荐用户的历史行为数据，并确定根据所述历史行为数据确定所对应的历史产品集，所述历史行为数据包括所述待推荐用户对历史产品的行为类型和行为发生时间；
兴趣确定模块，用于根据所述历史行为数据的行为类型和行为发生时间计算所述历史产品集中各历史产品的真实兴趣评分，并根据所述真实兴趣评分在所述历史产品集中确定所述待推荐用户的真实兴趣产品；
信息推送模块，用于获取与所述真实兴趣产品具有关联关系的推荐产品的推荐产品信息，并基于预设推荐规则向所述待推荐用户对应的用户终端推送所述推荐产品信息。
此外，为实现上述目的，本申请还提供一种基于用户行为的产品推荐设备，所述基于用户行为的产品推荐设备包括处理器、存储器、以及存储在所述存储器上并可被所述处理器执行的计算机可读指令，其中所述计算机可读指令被所述处理器执行时，实现如上述的基于用户行为的产品推荐方法的步骤。
此外，为实现上述目的，本申请还提供一种存储介质，所述存储介质上存储有计算机可读指令，其中所述计算机可读指令被处理器执行时，实现如上述的基于用户行为的产品推荐方法的步骤。
本申请基于用户的历史行为数据分析其真实兴趣，再根据真实兴趣产品获取与之关联的推荐产品并进行推送，使得产品推荐结果符合用户的实际需要，从而提升推荐效果；同时，在分析用户的真实兴趣时，基于用户行为类型和行为时间两个维度进行，从而可在一定程度上减少用户无意识行为浏览、广告、营销活动等因素引起的非真实兴趣行为数据(噪声)对用户兴趣分析造成的不利影响，还通过时间衰减的方式模拟了用户的兴趣衰减情况，进一步提高了兴趣分析的准确性。
附图说明
图1为本申请实施例方案中涉及的基于用户行为的产品推荐设备的硬件结构 示意图；
图2为本申请基于用户行为的产品推荐方法第一实施例的流程示意图；
图3为本申请基于用户行为的产品推荐方法第二实施例的流程示意图；
图4为本申请基于用户行为的产品推荐装置第一实施例的功能模块示意图。
本申请目的的实现、功能特点及优点将结合实施例，参照附图做进一步说明。
具体实施方式
应当理解，此处所描述的具体实施例仅仅用以解释本申请，并不用于限定本申请。
本申请实施例涉及的基于用户行为的产品推荐方法主要应用于基于用户行为的产品推荐设备，该产品推荐设备可以是个人计算机(personal computer，PC)、服务器等具有数据处理功能的设备实现的。
参照图1，图1为本申请实施例方案中涉及的基于用户行为的产品推荐设备的硬件结构示意图。本申请实施例中，该产品推荐设备可以包括处理器1001(例如中央处理器Central Processing Unit，CPU)，通信总线1002，用户接口1003，网络接口1004，存储器1005。其中，通信总线1002用于实现这些组件之间的连接通信；用户接口1003可以包括显示屏(Display)、输入单元比如键盘(Keyboard)；网络接口1004可选的可以包括标准的有线接口、无线接口(如无线保真WIreless-FIdelity，WI-FI接口)；存储器1005可以是高速随机存取存储器(random access memory，RAM)，也可以是稳定的存储器(non-volatile memory)，例如磁盘存储器，存储器1005可选的还可以是独立于前述处理器1001的存储装置。本领域技术人员可以理解，图1中示出的硬件结构并不构成对本申请的限定，可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件布置。
继续参照图1，图1中作为一种计算机可读存储介质的存储器1005可以包括操作系统、网络通信模块以及计算机可读指令。在图1中，网络通信模块主要用于连接数据库，与数据库进行数据通信；而处理器1001可以调用存储器1005中存储的计算机可读指令，并执行本申请实施例提供的基于用户行为的产品推荐方法。
本申请实施例提供了一种基于用户行为的产品推荐方法。
参照图2，图2为本申请基于用户行为的产品推荐方法第一实施例的流程示 意图。
本实施例中，所述基于用户行为的产品推荐方法包括以下步骤：
步骤S10，获取待推荐用户的历史行为数据，并确定根据所述历史行为数据确定所对应的历史产品集，所述历史行为数据包括所述待推荐用户对历史产品的行为类型和行为发生时间；
随着网络技术的发展，网络成为用户获取产品的主要平台之一。如何有效的向网络用户推荐产品，是产品提供商重点关注的问题。传统的产品推荐方法，是通过在互联网网站上大量投放产品广告，或是在某些产品平台的首页推荐位置推荐相同的当前主推产品，即向不同的用户推荐相同的产品。然而这种“广撒网”式宣传行为，由于针对对象以及推荐的产品不明确，且不同的用户对同一产品的关注度不同，导致这种推荐方法的效果很不理想；同时，无用的产品推送还会耗费用户比较多的精力和时间，甚至于还会引起用户的反感，从而影响了产品的推广。对此，本实施例提出了一种基于用户行为的产品推荐方法，根据用户的历史行为数据分析用户的真实兴趣，再根据真实兴趣产品获取与之关联的推荐产品并进行推送，使得产品推荐结果符合用户的实际需要，从而提升推荐效果。
本实施例的基于用户行为的产品推荐方法是由基于用户行为的产品推荐设备实现的，该设备以推荐服务器为例进行说明；而对于该产品，则可以是股票、基金、保险等金融产品，还可是其它数码产品、日用品等，本实施例中以金融产品为例进行说明。推荐服务器为了实现针对性的产品推荐，首先需要获取待推荐用户的历史行为数据，这些历史行为数据为待推荐用户在对一些产品的历史操作行为，其行为类型包括但不限于浏览、搜索、点击产品、收藏(关注)、分享、购买等；同时，历史行为数据中还包括有各历史行为的发生时间，以及各历史行为的行为对象产品，而各历史行为的行为对象产品的集合可称为历史产品集；例如待推荐用户在2018年x月y日z时浏览了A、B、C三款金融产品，其中B是通过搜索的方式浏览，且购买了B产品，关注了C产品，其中历史产品集包括A、B、C三款金融产品。
对于上述的历史行为数据获取，可以预先在终端应用app中内嵌统计软件开发工具包SDK(Software Development Kit)，当待推荐用户在自己的用户终端上安装该终端应用app时，通过该SDK请求获取待用户的操作行为信息；当待推荐用户同意时，用户终端将根据SDK的内在统计逻辑对用户的搜索、浏览等 行为进行记录，并结合待推荐用户的身份信息(例如终端IP地址、用户账户等)生成对应的历史行为数据，然后将该历史行为数据发送至推荐服务器中，此时，推荐服务器即可通过安装在用户终端的应用中的统计SDK获取到待推荐用户的历史行为数据。此外，还可以是在相关产品网站的脚本上进行设置埋点逻辑进行统计，当待推荐用户通过用户终端访问产品网站时，若满足某一统计条件，则该网站的网站服务器将获取当前访问的用户终端的身份信息、并记录其行为，从而生成历史行为数据等，并将这些历史行为数据发送至推荐服务器；而对于埋点逻辑的相关统计条件，则可以是根据实际情况进行设置，例如可以是页面停留时长超过预设时间、对页面的某个特定元素执行特定的操作(如点击、收藏等)、检索使用了特定关键字等。当然，在实际中，待推荐用户的历史行为数据也可以是由第三方机构进行收集，推荐服务器则是从该第三方机构获取这些历史行为数据。
步骤S20，根据所述历史行为数据的行为类型和行为发生时间计算所述历史产品集中各历史产品的真实兴趣评分，并根据所述真实兴趣评分在所述历史产品集中确定所述待推荐用户的真实兴趣产品；
本实施例中，对于待推荐用户的历史行为，在一定程度可以反映待推荐用户兴趣所在，因此推荐服务器在得到历史行为数据和历史产品集时，将根据该历史行为数据进行分析，以确定用户的兴趣产品。但值得说明的是，待推荐用户的历史行为不一定均为待推荐用户在其自身的兴趣(或需求)驱使下进行的操作，有可能是受到其它因素影响才促使待推荐用户进行的行为；例如待推荐用户是从其它的活动促销窗口进入到金融产品的浏览界面，该项历史行为是无意识的行为；又例如待推荐用户检索某个金融并非自己购买，而是为了给朋友进行检索；对于这些历史行为并一定不能完全反映用户的真实兴趣所在。同时，对于不同类型的历史行为，其对待推荐用户兴趣点的反映力度也不一定相同；例如待推荐用户购买了B产品，关注了C产品，浏览了A产品(未对A产品进行其它点击、关注等特别操作)，在这种情况下，待推荐用户对A、B、C三种产品的兴趣度可认为是不同的，其对B产品最感兴趣，其次分别为C产品和A产品。此外，对于历史行为的发生时间(或是数据采集时间)，对于用户兴趣的反映也具有一定的时效性。因此，本实施例在根据待推荐用户的历史行为数据分析其兴趣产品时，将综合历史行为数据的行为类型和行为发生时间两个维度进行分析，以行为类型和行为发生时间两个维度将待推荐用户的历史行为数据量化为对应的数值，并依据 一定的算法对该数值进行计算，得出待推荐用户曾经涉及到的各历史产品的真实兴趣评分，用以表征待推荐用户对这些历史产品的兴趣程度(或兴趣可能性)，并进一步根据该真实评分确定待推荐用户的真实兴趣产品；其中，某个历史的真实兴趣评分越高，则可认为待推荐用户对该历史产品的越感兴趣(或待推荐用户对该历史产品的越感兴趣的可能性越高)。
本实施例中，对于每条历史行为数据，都包括所对应历史行为的行为类型和行为发生时间，当然还包括历史行为的对象(涉及的金融产品)，这些历史行为的对象形成了一个历史产品集。而对于历史产品集中的每个历史产品，待推荐用户的每次历史行为都会为产品赋予一次真实兴趣分值加成；针对某一产品的历史行为次数越多，该产品的真实兴趣分值越高；例如，待推荐用户浏览了A产品2次，浏览了B产品5次，则B产品的真实兴趣分值要高于A产品的真实兴趣分值。当然，对于不同的历史行为，其对产品的真实兴趣分值加成程度具有区别，例如待推荐用户浏览了A产品1次，购买了B产品1次，而购买行为和浏览行为相比，购买行为所反映的兴趣程度要高于浏览行为的兴趣程度，因此A产品的真实兴趣分值要高于B产品的真实兴趣分值。而对于不同的历史行为的发生时间，则可设置一衰减函数，以表征该历史行为所产生的真实兴趣分值加成的衰减(也即表征待推荐用户对某一历史产品的兴趣衰减)。根据上述说明，推荐服务器可先统计出待推荐用户对各历史产品的行为类型和行为次数，然后将该行为行为类型和行为次数量化为对应的数值，并根据行为发生时间对数值进行衰减处理，从而得到各历史产品的真实兴趣评分。
具体的，根据所述历史行为数据的行为类型和行为发生时间计算所述历史产品集中各历史产品的真实兴趣评分的步骤包括：
根据所述历史行为数据所对应的历史产品对所述历史行为数据进行一次分类，得到各历史产品对应的产品类行为数据；
对于步骤S10获得的历史行为数据，推荐服务器可先以其行为对象(也即该行为对应的历史产品)进行分类，从而得到各历史产品对应的各类产品类行为数据。例如，A产品的产品类行为数据包括，2017年x1月y1日z1时浏览了A产品、2017年x1月y2日z2时购买了A产品；B产品的产品类行为数据为2017年x1月y1日z1时浏览了B产品，2017年x1月y3日z3时浏览了B产品。通过上述处理，可得到待推荐用户针对每种历史产品的行为数据。
分别统计所述产品类行为数据中各行为类型的行为次数，并确定各行为类型的最近行为时间；
在得到产品类行为数据时，推荐服务器将对各产品类数据进行统计分析，并确定各产品类行为数据中各行为类型的行为次数，以及确定各行为类型的最近行为时间(最后一次发生该行为的时间)。例如，对于A产品的产品类行为数据，包括M次浏览行为、N次购买行为，其中对A产品进行浏览行为的最近行为时间为2017年x1月y1日z1时，对A产品进行购买行为的最近行为时间为2017年x1月y2日z2时。通过上述处理，可得到待推荐用户针对每种历史产品的行为特征(包括各行为类型的行为次数和各行为类型的最近行为时间)。
分别将所述各历史产品对应的各行为类型的行为次数和各行为类型的最近行为时间代入至预设兴趣分公式中，以计算所述各历史产品的真实兴趣分值。
在得到待推荐用户对每种历史产品的行为类型的行为次数和各行为类型的最近行为时间时，可将该行为次数和最近行为时间代入至一预设兴趣分公式中，用以计算各历史产品的真实兴趣分值。如同上述，在该预设兴趣分公式，不同的行为类型具有不同的兴趣分值加成；同一行为类型的行为次数越多，该行为类型的兴趣分值加成越高；同时，每种行为类型的兴趣分值将受到时间衰减的影响。对此，该预设兴趣分公式可以为：
Figure PCTCN2019092100-appb-000001
其中，P为各历史产品的真实兴趣分值；
n为产品类行为数据中包括的行为类型的种类，n≥1；
Q i为产品类行为数据中第i种历史行为对应的预设分值加成，Q i≥0，i≥1；
K i为产品类行为数据中第i种历史行为的行为次数，K i≥0；
F(t i-t 0)为时间衰减函数，t i为所述产品类行为数据中第i种历史行为的最近行为时间，t 0为当前时间；F(t i-t 0)与t i-t 0负相关，t i-t 0越小，F(t i-t 0)越大；例如F(t i-t 0)＝mlog(t i-t 0)；又例如
Figure PCTCN2019092100-appb-000002
其中e为自然对数，λ为大于0的常数。
值得说明的是，对于上述预设兴趣分公式也可以根据实际情况进行调整和变换。
本实施例中，在计算得到历史产品集中各历史产品对应的真实兴趣评分时，即可将真实兴趣评分最高的若干个历史产品确定为待推荐用户的真实兴趣产品(当然对于真实兴趣产品的数量可以根据实际情况自行定义)；又或者是将真实兴趣分值高于一预设兴趣阈值的历史产品作为真实兴趣产品(当不存在真实兴趣分值高于一预设兴趣阈值的历史产品时，可随机将一历史产品作为真实兴趣产品，或是将真实兴趣分值最高的历史产品作为真实兴趣产品)。此外，若历史产品集中涉及多种不同类型的历史产品，还可分别确定该类型的真实兴趣产品，例如待推荐用户的金融历史产品包括股票和基金产品，则可分别确定股票类的真实兴趣产品和基金类的真实兴趣产品。
步骤S30，获取与所述真实兴趣产品具有关联关系的推荐产品的推荐产品信息，并基于预设推荐规则向所述待推荐用户对应的用户终端推送所述推荐产品信息。
本实施例中，推荐服务器确定待推荐用户的真实兴趣产品时，即可根据该真实兴趣产品进行针对性的产品查询，获取与该真实兴趣产品具有关联关系的推荐产品，并获取该推荐产品的推荐产品信息。其中，对于该关联关系，在不同类型的真实兴趣产品中可以是有不同的体现。例如，对于股票、基金、保险等金融产品，该关联关系可以金额范围类似、运营机构相同、风险等级类似等；对于数码产品，该关联关系可以是功能相同、价位相似、品牌相同等。本实施例中，推荐服务器在获取到推荐产品产品的推荐产品信息时，即可根据一预设推荐规则向推荐用户的用户终端推送该推荐产品信息。其中，该预设推荐规则可以包括推荐时间、推送频率、推送数据量等内容的规定。
可选地，当真实兴趣产品的产品类型为基金时，所述获取与所述真实兴趣产品具有关联关系的推荐产品的推荐产品信息步骤包括：
确定所述真实兴趣产品的基金风险类型和所述真实兴趣产品持有的重仓股票，并确定所述重仓股票的行业类型；
本实施例中，当真实兴趣产品的产品类型为基金时，推荐服务器可先确定该真实兴趣产品(基金)的基金风险类型，如保守型、稳健型、进取型等，其中不同的基金风险类型对应不同的风险等级。同时推荐服务器还将获取该真实兴趣产品(基金)的股票持有信息，该股票持有信息包括股票名称、各股票发行者所属行业、各股票持有市值等；然后可根据该股票持有信息确定该真实兴趣产品的 重仓股票，其中该重仓股票为持有市场最高的股票。在确定重仓股票时，推荐服务器还将确定该重仓股票的行业类型(即股票发行者所属行业)。
查询所述行业类型对应的可选股票，并根据所述可选股票在预设周期的股价变化确定所述可选股票的股票风险类型；
本实施例中，当确定重仓股票的行业类型时，推荐服务器将查询该行业类型的可选股票，并获取这些可选股票在预设周期内的股价变化信息，然后根据这些股价变化信息确定这些可选股票类型的股票风险类型。例如，以“一周”为周期，若某一股票的股价极值波动范围在小于5％，则该股票的股票风险类型为保守型；若该股票的股价极值波动范围在5％到10％之间，则该股票的股票风险类型为稳健型；若某一股票的股价极值波动范围在大于10％，则该股票的股票风险类型为进取型等。
根据所述股票风险类型和基金风险类型在所述可选股票中确定与所述真实兴趣产品关联的推荐股票，并将所述推荐股票确定为推荐产品；
本实施例中，当确定各可选股票的股票风险类型时，可根据股票风险类型和基金风险类型确定与所述真实兴趣产品具有相同(或相似)风险等级的可选股票，例如同为保守型、同为稳健型等；该可选股票即为与所述真实兴趣产品关联的推荐股票，并将该推荐股票确定为推荐产品。
获取所述推荐产品的推荐产品信息。
本实施例中，在确定推荐产品时，推荐服务器即可获取该推荐产品的推荐产品信息，用以向待推荐用户进行推送。
以上通过从待推荐用户的兴趣基金中确定重仓股票，再从该重仓股票的所属行业中选择推荐股票，从而实现了推荐产品与兴趣产品的行业领域相似，还有利于降低兴趣基金的运营机构的日常运营操作对推荐股票造成的不利影响；而且在选择推荐股票时还将会考虑用户的风险承受等级，提高推荐产品与用户的兴趣贴合度。
可选地，当真实兴趣产品的产品类型为股票时，所述获取与所述真实兴趣产品具有关联关系的推荐产品的推荐产品信息步骤包括：
确定最高额持有所述真实兴趣产品的最高额基金，并确定所述最高额基金的运营机构；
本实施例中，当真实兴趣产品的产品类型为股票时，推荐服务器可先确定 该股票的持有者，其中该股票的持有可能包括基金机构、个人、公司等；然后推荐服务器可在这些持有者中确定最高额持有该股票的最高额基金，也即拥有该股票的份额最多基金，并确定该最高额基金的运营机构。
查询所述运营机构运营的可选基金，并将所述可选基金确定为推荐产品；
在确定该股票的最高额基金的运营机构时，推荐服务器将查询该运营机构所运营的所有可选(可购买)基金，并将这些可选基金作为推荐产品。
获取所述推荐产品的推荐产品信息。
本实施例中，在确定推荐产品时，推荐服务器即可获取该推荐产品的推荐产品信息，用以向待推荐用户进行推送。
以上通过推荐高额持有兴趣股票的基金运营机构的基金产品，以运营方的角度让用户了解与该股票的其它基金产品，方便用户获取到其需求产品，提升推荐效果。
本实施例中，为了进一步提高产品推荐的效果，还可以根据历史行为数据分析待推荐用户的浏览习惯，进而根据用户的浏览习惯进行针对性的产品推荐。具体的，推荐服务器可以对待推荐用户的历史行为数据进行分析，从而获取所述待推荐用户的高频浏览时段；例如待推荐用户在过去7天内有5天在中午12点至12点30分、晚上22点至22点20分浏览产品，则可认为目标用户的高频浏览时段为中午12点至12点30分、晚上22点至22点20分。当然，对于不同的高频浏览时段，对应的时段时长可能不同，待推荐用户所能查看的信息量也不同，因此，推荐服务器还将确定各高频浏览时段的时段时长。
当检测到当前时间处于高频浏览时段时，推荐服务器将根据当前所处的高频浏览时段的时段时长确定推荐信息量；其中对于该推荐信息量与时段时长的关系，可以是预置一规则进行设置，例如10分钟的时长对应1个产品，20分钟的时长对应3个产品等，当然该推荐信息量也可以是根据产品信息的类型进行表征，例如10分钟的时长对应产品名称和简介，20分钟的时长对应该产品的详细介绍等。当确定推荐信息量时，推荐服务器即可根据该推荐信息量向待推荐用户的用户终端推送对应的推荐产品信息。通过以上方式，可使得产品推荐的时间和信息量能够更贴近待推荐用户的浏览习惯，减少无效推送导致用户反感的情况，有利于提升推荐效果。
本实施例中，通过获取待推荐用户的历史行为数据，并确定根据所述历史 行为数据确定所对应的历史产品集，所述历史行为数据包括所述待推荐用户对历史产品的行为类型和行为发生时间；根据所述历史行为数据的行为类型和行为发生时间计算所述历史产品集中各历史产品的真实兴趣评分，并根据所述真实兴趣评分在所述历史产品集中确定所述待推荐用户的真实兴趣产品；获取与所述真实兴趣产品具有关联关系的推荐产品的推荐产品信息，并基于预设推荐规则向所述待推荐用户对应的用户终端推送所述推荐产品信息。通过以上形式，本实施例基于用户的历史行为数据分析其真实兴趣，再根据真实兴趣产品获取与之关联的推荐产品并进行推送，使得产品推荐结果符合用户的实际需要，从而提升推荐效果；同时，在分析用户的真实兴趣时，基于用户行为类型和行为时间两个维度进行，从而可在一定程度上减少用户无意识行为浏览、广告、营销活动等因素引起的非真实兴趣行为数据(噪声)对用户兴趣分析造成的不利影响，还通过时间衰减的方式模拟了用户的兴趣衰减情况，进一步提高了兴趣分析的准确性。
参照图3，图3为本申请基于用户行为的产品推荐方法第二实施例的流程示意图。
基于上述图2所示实施例，本实施例中，所述推荐产品信息包括人工服务链接，步骤S30之后还包括：
步骤S40，在接收到所述用户终端基于所述人工服务链接发送的人工服务请求时，根据所述推荐产品信息查询对应的人工客服端，并向所述人工客服端发送对应的服务任务信息。
本实施例中，考虑到待推荐用户在浏览了推送的推荐产品信息后，可能会有疑问，为了方便待推荐用户进行咨询，本实施例中还可以为待推荐用户提供人工咨询服务。具体的，推荐服务器所推送的推荐产品信息中包括有人工服务链接；待推荐用户在通过用户终端浏览了推送的推荐产品信息后，若需要向客服人员进行人工咨询，则可通过用户终端点击该人工服务链接，从而触发对于的人工服务请求；用户终端根据待推荐用户的操作将该人工服务请求发送至推荐服务器。推荐服务器在接收到该人工服务请求时，首先将根据该推荐产品信息查询对应的人工客服端(负责该信贷产品的业务人员、产品经理等人的终端)，并向所述人工客服端发送对应的服务任务信息；其中该服务任务信息可以包括用户终端的IP地址、账户名称、电话号码等，从而使得客服人员能够通过人工客服端与待推荐用户进行联系，为目标用户提供人工服务，提升目标用户的服务体验。
此外，本申请实施例还提供一种基于用户行为的产品推荐装置。
参照图4，图4为本申请基于用户行为的产品推荐装置第一实施例的功能模块示意图。
本实施例中，所述基于用户行为的产品推荐装置包括：
数据获取模块10，用于获取待推荐用户的历史行为数据，并确定根据所述历史行为数据确定所对应的历史产品集，所述历史行为数据包括所述待推荐用户对历史产品的行为类型和行为发生时间；
兴趣确定模块20，用于根据所述历史行为数据的行为类型和行为发生时间计算所述历史产品集中各历史产品的真实兴趣评分，并根据所述真实兴趣评分在所述历史产品集中确定所述待推荐用户的真实兴趣产品；
信息推送模块30，用于获取与所述真实兴趣产品具有关联关系的推荐产品的推荐产品信息，并基于预设推荐规则向所述待推荐用户对应的用户终端推送所述推荐产品信息。
其中，上述基于用户行为的产品推荐装置的各虚拟功能模块存储于图1所示基于用户行为的产品推荐设备的存储器1005中，用于实现计算机可读指令的所有功能；各模块被处理器1001执行时，可实现获取用户历史行为数据，并从这些历史行为数据中分析用户的兴趣产品，并根据该兴趣产品进行关联产品推送的功能。
进一步的，所述兴趣确定模块20包括：
数据分类单元，用于根据所述历史行为数据所对应的历史产品对所述历史行为数据进行分类，得到各历史产品对应的产品类行为数据；
数据统计单元，用于分别统计所述产品类行为数据中各行为类型的行为次数，并确定各行为类型的最近行为时间；
分值计算单元，用于分别将所述各历史产品对应的各行为类型的行为次数和各行为类型的最近行为时间代入至预设兴趣分公式中，以计算所述各历史产品的真实兴趣分值。
进一步的，所述预设兴趣分公式为：
Figure PCTCN2019092100-appb-000003
其中，P为所述各历史产品的真实兴趣分值；
n为所述产品类行为数据中包括的行为类型的种类，n≥1；
Q i为所述产品类行为数据中第i种历史行为对应的预设分值加成，Q i≥0，i≥1；
K i为所述产品类行为数据中第i种历史行为的行为次数，K i≥0；
F(t i-t 0)为时间衰减函数，t i为所述产品类行为数据中第i种历史行为的最近行为时间，t 0为当前时间；F(t i-t 0)与t i-t 0负相关。
进一步的，所述真实兴趣产品的产品类型为基金，所述兴趣确定模块20包括：
第一确定单元，确定所述真实兴趣产品的基金风险类型和所述真实兴趣产品持有的重仓股票，并确定所述重仓股票的行业类型；
第二确定单元，用于查询所述行业类型对应的可选股票，并根据所述可选股票在预设周期的股价变化确定所述可选股票的股票风险类型；
第三确定单元，用于根据所述股票风险类型和基金风险类型在所述可选股票中确定与所述真实兴趣产品关联的推荐股票，并将所述推荐股票确定为推荐产品；
第一获取单元，用于获取所述推荐产品的推荐产品信息。
进一步的，所述真实兴趣产品的产品类型为股票，所述兴趣确定模块20包括：
第四确定单元，用于确定最高额持有所述真实兴趣产品的最高额基金，并确定所述最高额基金的运营机构；
第五确定单元，用于查询所述运营机构运营的可选基金，并将所述可选基金确定为推荐产品；
第二获取单元，用于获取所述推荐产品的推荐产品信息。
进一步的，所述信息推送模块30包括：
时段获取单元，用于根据所述历史行为数据获取所述待推荐用户的高频浏览时段，并确定所述高频浏览时段的时段时长；
信息推送单元，用于当当前时间处于所述高频浏览时段时，根据当前所处高频浏览时段的时段时长确定推荐信息量，并根据所述推荐信息量向所述待推荐用户的用户终端推送所述推荐产品信息。
进一步的，所述推荐产品信息包括人工服务链接，所述基于用户行为的产 品推荐装置还包括：
任务发送模块，用于在接收到所述用户终端基于所述人工服务链接发送的人工服务请求时，根据所述推荐产品信息查询对应的人工客服端，并向所述人工客服端发送对应的服务任务信息。
其中，上述基于用户行为的产品推荐装置中各个模块的功能实现与上述基于用户行为的产品推荐方法实施例中各步骤相对应，其功能和实现过程在此处不再一一赘述。
此外，本申请实施例还提供一种存储介质，所述存储介质可以为非易失性可读存储介质。
本申请存储介质上存储有计算机可读指令，其中所述计算机可读指令被处理器执行时，实现如上述的基于用户行为的产品推荐方法的步骤。
其中，计算机可读指令被执行时所实现的方法可参照本申请基于用户行为的产品推荐方法的各个实施例，此处不再赘述。
需要说明的是，在本文中，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者系统不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者系统所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括该要素的过程、方法、物品或者系统中还存在另外的相同要素。
上述本申请实施例序号仅仅为了描述，不代表实施例的优劣。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到上述实施例方法可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件，但很多情况下前者是更佳的实施方式。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品存储在如上所述的一个存储介质(如ROM/RAM、磁碟、光盘)中，包括若干指令用以使得一台终端设备(可以是手机，计算机，服务器，空调器，或者网络设备等)执行本申请各个实施例所述的方法。
以上仅为本申请的优选实施例，并非因此限制本申请的专利范围，凡是利用本申请说明书及附图内容所作的等效结构或等效流程变换，或直接或间接运用在其他相关的技术领域，均同理包括在本申请的专利保护范围内。

Claims (20)

  1. 一种基于用户行为的产品推荐方法，其特征在于，所述基于用户行为的产品推荐方法包括：
    获取待推荐用户的历史行为数据，并根据所述历史行为数据确定对应的历史产品集，所述历史行为数据包括所述待推荐用户对历史产品的行为类型和行为发生时间；
    根据所述历史行为数据的行为类型和行为发生时间计算所述历史产品集中各历史产品的真实兴趣评分，并根据所述真实兴趣评分在所述历史产品集中确定所述待推荐用户的真实兴趣产品；
    获取与所述真实兴趣产品具有关联关系的推荐产品的推荐产品信息，并基于预设推荐规则向所述待推荐用户对应的用户终端推送所述推荐产品信息。
  2. 如权利要求1所述的基于用户行为的产品推荐方法，其特征在于，所述根据所述历史行为数据的行为类型和行为发生时间计算所述历史产品集中各历史产品的真实兴趣评分的步骤包括：
    根据所述历史行为数据所对应的历史产品对所述历史行为数据进行分类，得到各历史产品对应的产品类行为数据；
    分别统计所述产品类行为数据中各行为类型的行为次数，并确定各行为类型的最近行为时间；
    分别将所述各历史产品对应的各行为类型的行为次数和各行为类型的最近行为时间代入至预设兴趣分公式中，以计算所述各历史产品的真实兴趣分值。
  3. 如权利要求2所述的基于用户行为的产品推荐方法，其特征在于，所述预设兴趣分公式为：
    Figure PCTCN2019092100-appb-100001
    其中，P为所述各历史产品的真实兴趣分值；
    n为所述产品类行为数据中包括的行为类型的种类，n≥1；
    Q i为所述产品类行为数据中第i种历史行为对应的预设分值加成，Q i≥0，i≥1；
    K i为所述产品类行为数据中第i种历史行为的行为次数，K i≥0；
    F(t i-t 0)为时间衰减函数，t i为所述产品类行为数据中第i种历史行为的最近行为时间，t 0为当前时间；F(t i-t 0)与t i-t 0负相关。
  4. 如权利要求1所述的基于用户行为的产品推荐方法，其特征在于，所述真实兴趣产品的产品类型为基金，
    所述获取与所述真实兴趣产品具有关联关系的推荐产品的推荐产品信息步骤包括：
    确定所述真实兴趣产品的基金风险类型和所述真实兴趣产品持有的重仓股票，并确定所述重仓股票的行业类型；
    查询所述行业类型对应的可选股票，并根据所述可选股票在预设周期的股价变化确定所述可选股票的股票风险类型；
    根据所述股票风险类型和基金风险类型在所述可选股票中确定与所述真实兴趣产品关联的推荐股票，并将所述推荐股票确定为推荐产品；
    获取所述推荐产品的推荐产品信息。
  5. 如权利要求1所述的基于用户行为的产品推荐方法，其特征在于，所述真实兴趣产品的产品类型为股票，
    所述获取与所述真实兴趣产品具有关联关系的推荐产品的推荐产品信息步骤包括：
    确定最高额持有所述真实兴趣产品的最高额基金，并确定所述最高额基金的运营机构；
    查询所述运营机构运营的可选基金，并将所述可选基金确定为推荐产品；
    获取所述推荐产品的推荐产品信息。
  6. 如权利要求1所述的基于用户行为的产品推荐方法，其特征在于，所述基于预设推荐规则向所述待推荐用户对应的用户终端推送所述推荐产品信息的步骤包括：
    根据所述历史行为数据获取所述待推荐用户的高频浏览时段，并确定所述高频浏览时段的时段时长；
    当当前时间处于所述高频浏览时段时，根据当前所处高频浏览时段的时段时长确定推荐信息量，并根据所述推荐信息量向所述待推荐用户的用户终端推送所述推荐产品信息。
  7. 如权利要求1所述的基于用户行为的产品推荐方法，其特征在于，所述 推荐产品信息包括人工服务链接，
    所述获取与所述真实兴趣产品具有关联关系的推荐产品的推荐产品信息，并基于预设推荐规则向所述待推荐用户对应的用户终端推送所述推荐产品信息的步骤之后，还包括：
    在接收到所述用户终端基于所述人工服务链接发送的人工服务请求时，根据所述推荐产品信息查询对应的人工客服端，并向所述人工客服端发送对应的服务任务信息。
  8. 一种基于用户行为的产品推荐装置，其特征在于，所述基于用户行为的产品推荐装置包括：
    数据获取模块，用于获取待推荐用户的历史行为数据，并确定根据所述历史行为数据确定所对应的历史产品集，所述历史行为数据包括所述待推荐用户对历史产品的行为类型和行为发生时间；
    兴趣确定模块，用于根据所述历史行为数据的行为类型和行为发生时间计算所述历史产品集中各历史产品的真实兴趣评分，并根据所述真实兴趣评分在所述历史产品集中确定所述待推荐用户的真实兴趣产品；
    信息推送模块，用于获取与所述真实兴趣产品具有关联关系的推荐产品的推荐产品信息，并基于预设推荐规则向所述待推荐用户对应的用户终端推送所述推荐产品信息。
  9. 如权利要求8所述的基于用户行为的产品推荐装置，其特征在于，所述兴趣确定模块包括：
    数据分类单元，用于根据所述历史行为数据所对应的历史产品对所述历史行为数据进行分类，得到各历史产品对应的产品类行为数据；
    数据统计单元，用于分别统计所述产品类行为数据中各行为类型的行为次数，并确定各行为类型的最近行为时间；
    分值计算单元，用于分别将所述各历史产品对应的各行为类型的行为次数和各行为类型的最近行为时间代入至预设兴趣分公式中，以计算所述各历史产品的真实兴趣分值。
  10. 如权利要求9所述的基于用户行为的产品推荐装置，其特征在于，所述预设兴趣分公式为：
    Figure PCTCN2019092100-appb-100002
    其中，P为所述各历史产品的真实兴趣分值；
    n为所述产品类行为数据中包括的行为类型的种类，n≥1；
    Q i为所述产品类行为数据中第i种历史行为对应的预设分值加成，Q i≥0，i≥1；
    K i为所述产品类行为数据中第i种历史行为的行为次数，K i≥0；
    F(t i-t 0)为时间衰减函数，t i为所述产品类行为数据中第i种历史行为的最近行为时间，t 0为当前时间；F(t i-t 0)与t i-t 0负相关。
  11. 如权利要求8所述的基于用户行为的产品推荐装置，其特征在于，所述真实兴趣产品的产品类型为基金，所述兴趣确定模块包括：
    第一确定单元，确定所述真实兴趣产品的基金风险类型和所述真实兴趣产品持有的重仓股票，并确定所述重仓股票的行业类型；
    第二确定单元，用于查询所述行业类型对应的可选股票，并根据所述可选股票在预设周期的股价变化确定所述可选股票的股票风险类型；
    第三确定单元，用于根据所述股票风险类型和基金风险类型在所述可选股票中确定与所述真实兴趣产品关联的推荐股票，并将所述推荐股票确定为推荐产品；
    第一获取单元，用于获取所述推荐产品的推荐产品信息。
  12. 如权利要求8所述的基于用户行为的产品推荐装置，其特征在于，所述真实兴趣产品的产品类型为股票，所述兴趣确定模块包括：
    第四确定单元，用于确定最高额持有所述真实兴趣产品的最高额基金，并确定所述最高额基金的运营机构；
    第五确定单元，用于查询所述运营机构运营的可选基金，并将所述可选基金确定为推荐产品；
    第二获取单元，用于获取所述推荐产品的推荐产品信息。
  13. 如权利要求8所述的基于用户行为的产品推荐装置，其特征在于，所述信息推送模块包括：
    时段获取单元，用于根据所述历史行为数据获取所述待推荐用户的高频浏览时段，并确定所述高频浏览时段的时段时长；
    信息推送单元，用于当当前时间处于所述高频浏览时段时，根据当前所处高频浏览时段的时段时长确定推荐信息量，并根据所述推荐信息量向所述待推荐用户的用户终端推送所述推荐产品信息。
  14. 如权利要求8所述的基于用户行为的产品推荐装置，其特征在于，所述推荐产品信息包括人工服务链接，所述基于用户行为的产品推荐装置还包括：
    任务发送模块，用于在接收到所述用户终端基于所述人工服务链接发送的人工服务请求时，根据所述推荐产品信息查询对应的人工客服端，并向所述人工客服端发送对应的服务任务信息。
  15. 一种基于用户行为的产品推荐设备，其特征在于，所述基于用户行为的产品推荐设备包括处理器、存储器、以及存储在所述存储器上并可被所述处理器执行的计算机可读指令，其中所述计算机可读指令被所述处理器执行时，实现如下步骤：
    获取待推荐用户的历史行为数据，并根据所述历史行为数据确定对应的历史产品集，所述历史行为数据包括所述待推荐用户对历史产品的行为类型和行为发生时间；
    根据所述历史行为数据的行为类型和行为发生时间计算所述历史产品集中各历史产品的真实兴趣评分，并根据所述真实兴趣评分在所述历史产品集中确定所述待推荐用户的真实兴趣产品；
    获取与所述真实兴趣产品具有关联关系的推荐产品的推荐产品信息，并基于预设推荐规则向所述待推荐用户对应的用户终端推送所述推荐产品信息。
  16. 如权利要求15所述的基于用户行为的产品推荐设备，其特征在于，所述根据所述历史行为数据的行为类型和行为发生时间计算所述历史产品集中各历史产品的真实兴趣评分的步骤包括：
    根据所述历史行为数据所对应的历史产品对所述历史行为数据进行分类，得到各历史产品对应的产品类行为数据；
    分别统计所述产品类行为数据中各行为类型的行为次数，并确定各行为类型的最近行为时间；
    分别将所述各历史产品对应的各行为类型的行为次数和各行为类型的最近行为时间代入至预设兴趣分公式中，以计算所述各历史产品的真实兴趣分值。
  17. 如权利要求16所述的基于用户行为的产品推荐设备，其特征在于，所 述预设兴趣分公式为：
    Figure PCTCN2019092100-appb-100003
    其中，P为所述各历史产品的真实兴趣分值；
    n为所述产品类行为数据中包括的行为类型的种类，n≥1；
    Q i为所述产品类行为数据中第i种历史行为对应的预设分值加成，Q i≥0，i≥1；
    K i为所述产品类行为数据中第i种历史行为的行为次数，K i≥0；
    F(t i-t 0)为时间衰减函数，t i为所述产品类行为数据中第i种历史行为的最近行为时间，t 0为当前时间；F(t i-t 0)与t i-t 0负相关。
  18. 一种存储介质，其特征在于，所述存储介质上存储有计算机可读指令，其中所述计算机可读指令被处理器执行时，实现如下步骤：
    获取待推荐用户的历史行为数据，并根据所述历史行为数据确定对应的历史产品集，所述历史行为数据包括所述待推荐用户对历史产品的行为类型和行为发生时间；
    根据所述历史行为数据的行为类型和行为发生时间计算所述历史产品集中各历史产品的真实兴趣评分，并根据所述真实兴趣评分在所述历史产品集中确定所述待推荐用户的真实兴趣产品；
    获取与所述真实兴趣产品具有关联关系的推荐产品的推荐产品信息，并基于预设推荐规则向所述待推荐用户对应的用户终端推送所述推荐产品信息。
  19. 如权利要求18所述的存储介质，其特征在于，所述根据所述历史行为数据的行为类型和行为发生时间计算所述历史产品集中各历史产品的真实兴趣评分的步骤包括：
    根据所述历史行为数据所对应的历史产品对所述历史行为数据进行分类，得到各历史产品对应的产品类行为数据；
    分别统计所述产品类行为数据中各行为类型的行为次数，并确定各行为类型的最近行为时间；
    分别将所述各历史产品对应的各行为类型的行为次数和各行为类型的最近行为时间代入至预设兴趣分公式中，以计算所述各历史产品的真实兴趣分值。
  20. 如权利要求18所述的存储介质，其特征在于，所述预设兴趣分公式为：
    Figure PCTCN2019092100-appb-100004
    其中，P为所述各历史产品的真实兴趣分值；
    n为所述产品类行为数据中包括的行为类型的种类，n≥1；
    Q i为所述产品类行为数据中第i种历史行为对应的预设分值加成，Q i≥0，i≥1；
    K i为所述产品类行为数据中第i种历史行为的行为次数，K i≥0；F(t i-t 0)为时间衰减函数，t i为所述产品类行为数据中第i种历史行为的最近行为时间，t 0为当前时间；F(t i-t 0)与t i-t 0负相关。
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