WO2018233301A1 - 产品推荐方法、装置、设备以及计算机可读存储介质 - Google Patents

产品推荐方法、装置、设备以及计算机可读存储介质 Download PDF

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Publication number
WO2018233301A1
WO2018233301A1 PCT/CN2018/076196 CN2018076196W WO2018233301A1 WO 2018233301 A1 WO2018233301 A1 WO 2018233301A1 CN 2018076196 W CN2018076196 W CN 2018076196W WO 2018233301 A1 WO2018233301 A1 WO 2018233301A1
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Prior art keywords
product
score
recommended
user
predicted
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English (en)
French (fr)
Inventor
丁家琳
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Ping An Technology Shenzhen Co Ltd
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Ping An Technology Shenzhen Co Ltd
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Priority to US16/305,887 priority Critical patent/US20200134693A1/en
Priority to JP2018559966A priority patent/JP6706348B2/ja
Publication of WO2018233301A1 publication Critical patent/WO2018233301A1/zh
Anticipated expiration legal-status Critical
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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—Recommending goods or services
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0202—Market predictions or forecasting for commercial activities
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0207—Discounts or incentives, e.g. coupons or rebates
    • G06Q30/0239—Online discounts or incentives
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/06—Asset management; Financial planning or analysis

Definitions

  • the present application relates to the field of Internet technologies, and in particular, to a product recommendation method, apparatus, device, and computer readable storage medium.
  • the main purpose of the present application is to provide a product recommendation method, apparatus, device and computer readable storage medium, aiming at solving the technical problem of low product purchase rate and low renewal product renewal rate.
  • the present application provides a product recommendation method, and the product recommendation method includes the steps of:
  • the product to be recommended is recommended to the user.
  • the present application further provides a product recommendation device, where the product recommendation device includes:
  • An obtaining module configured to: when detecting a triggering instruction for recommending a product to be recommended, acquiring, according to the triggering instruction, operation data of a user who has successfully purchased the product to be recommended;
  • a calculation module configured to calculate, according to the operation data, a predicted score that the user purchases the product to be recommended again;
  • a recommendation module configured to recommend the product to be recommended to the user if the predicted score is greater than a preset score.
  • the present application further provides a product recommendation device, which includes a memory, a processor, and a product recommendation program stored on the memory and operable on the processor, The steps of the product recommendation method as described above are implemented when the product recommendation program is executed by the processor.
  • the present application further provides a computer readable storage medium, where the product recommendation program is stored, and when the product recommendation program is executed by the processor, the product recommendation as described above is implemented. The steps of the method.
  • the application obtains the operation data of the user who has successfully purchased the product to be recommended according to the triggering instruction; and calculates, according to the operation data, the user to purchase the to-be recommended again. a predicted score of the product; if the predicted score is greater than a preset score, recommending the product to be recommended to the user. Realizing the calculation of the predicted score of the user to purchase the product to be recommended again according to the operation data of the user, determining whether to recommend the product to be recommended to the user according to the predicted score, improving the purchase rate of the product to be recommended; and for the product requiring renewal , increased the renewal rate of the renewed products.
  • FIG. 1 is a schematic structural diagram of a device in a hardware operating environment involved in an embodiment of the present application
  • FIG. 2 is a schematic flow chart of a first embodiment of a product recommendation method according to the present application.
  • FIG. 3 is a schematic flow chart of a second embodiment of a product recommendation method according to the present application.
  • FIG. 4 is a schematic flowchart of recommending the product to be recommended to the user if the predicted score is greater than a preset score in the embodiment of the present application.
  • FIG. 1 is a schematic structural diagram of a device in a hardware operating environment according to an embodiment of the present application.
  • the product recommendation device in the embodiment of the present application may be a PC, or may be a smart phone, a tablet computer, an e-book reader, and an MP3 (Moving). Picture Experts Group Audio Layer III, motion picture expert compression standard audio layer 3) player, MP4 (Moving Picture Experts Group Audio Layer IV, dynamic video experts compress standard audio layers 4) portable terminal devices such as players and portable computers.
  • MP3 Motion Picture Experts Group Audio Layer III, motion picture expert compression standard audio layer 3
  • MP4 Moving Picture Experts Group Audio Layer IV, dynamic video experts compress standard audio layers 4
  • portable terminal devices such as players and portable computers.
  • the product recommendation device may include a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, and a communication bus 1002.
  • the communication bus 1002 is used to implement connection communication between these components.
  • the user interface 1003 can include a display, an input unit such as a keyboard, and the optional user interface 1003 can also include a standard wired interface, a wireless interface.
  • the network interface 1004 can optionally include a standard wired interface, a wireless interface (such as a WI-FI interface).
  • the memory 1005 may be a high speed RAM memory or a stable memory (non-volatile) Memory), such as disk storage.
  • the memory 1005 can also optionally be a storage device independent of the aforementioned processor 1001.
  • the product recommendation device may also include a camera, RF (Radio) Frequency, RF) circuits, sensors, audio circuits, WiFi modules, and more.
  • RF Radio
  • RF Radio
  • the product recommendation device structure illustrated in FIG. 1 does not constitute a limitation to the terminal, and may include more or less components than those illustrated, or a combination of certain components, or different component arrangements.
  • an operating system and a product recommendation program may be included in the memory 1005 as a computer storage medium.
  • the operating system is a program that manages and controls the hardware and software resources of the product recommendation device, and supports the operation of the product recommendation program and other software and/or programs.
  • the network interface 1004 is mainly used to connect the terminal held by the user and perform data communication with the terminal held by the user; the user interface 1003 is mainly used to receive an acquisition instruction and the like.
  • the processor 1001 can be used to call the product recommendation program stored in the memory 1005 and perform the steps of the following product recommendation method.
  • FIG. 2 is a schematic flowchart of a first embodiment of a product recommendation method according to the present application.
  • the product recommendation methods include:
  • step S10 when the triggering instruction for recommending the product to be recommended is detected, the operation data of the user who has successfully purchased the product to be recommended is obtained according to the triggering instruction.
  • the operation data of the user who has successfully purchased the product to be recommended is obtained according to the trigger instruction.
  • the processor 1001 of the product recommendation device acquires, from the memory 1005, the operation data of the user who has successfully purchased the product to be recommended according to the triggering instruction.
  • the operation data includes, but is not limited to, the frequency of attention of the user to each product in the application in which the recommended product is located, the purchase amount of each product in the application, the payment data corresponding to the purchased product, and the number of clicks of the product to be recommended.
  • the triggering instruction may be automatically triggered by the product recommendation device, or may be manually triggered by the worker.
  • a timing task may be set in the product recommendation device (for example, the trigger command may be triggered to be triggered every day, or the trigger command may be triggered after a certain period of time), when the timing is reached.
  • the product recommendation device automatically triggers the trigger command when the condition of the task is met.
  • the successfully purchased product to be recommended indicates that the user has purchased the product to be recommended, and has paid the fee corresponding to the product to be recommended.
  • Step S20 Calculate, according to the operation data, a predicted score that the user purchases the product to be recommended again.
  • Step S30 If the predicted score is greater than the preset score, recommend the product to be recommended to the user.
  • the predicted score of the user to purchase the product to be recommended is calculated according to the operation data, and it is determined whether the predicted score is greater than the preset score.
  • the product to be recommended is recommended to the user according to a preset manner; when the predicted score is less than or equal to the preset score, the product to be recommended is not recommended to the user.
  • step S20 may further include:
  • step a based on the frequency of interest, the purchase amount, the payment data, and the number of clicks, the predicted sub-score corresponding to the frequency of interest, the purchase amount, the payment data, and the number of clicks are respectively calculated according to the corresponding preset rule.
  • step b the weights of the frequency of interest, the purchase amount, the payment data, and the number of clicks are determined.
  • Step c Calculate, according to the predicted sub-score and the weight, a predicted score that the user purchases the product to be recommended again.
  • the weight corresponding to the frequency of interest is 0.25, the weight corresponding to the purchase amount is 0.2, the weight corresponding to the payment data is 0.25, and the weight corresponding to the number of clicks is 0.3, if the frequency of interest is corresponding to
  • the predicted sub-scores of the frequency of interest are calculated according to the preset rules corresponding to the frequency of interest, and the prediction corresponding to the purchase amount is calculated according to the preset rule corresponding to the purchase amount.
  • the sub-scores are calculated according to the preset rules corresponding to the payment data, and the predicted sub-scores corresponding to the payment data are calculated according to the preset rule corresponding to the number of clicks.
  • the predicted sub-score corresponding to the frequency of interest, the purchase amount, the payment data, and the number of clicks After obtaining the predicted sub-score corresponding to the frequency of interest, the purchase amount, the payment data, and the number of clicks, determining the weight of the frequency of interest, the purchase amount, the payment data, and the number of clicks in calculating the predicted score, according to the frequency of interest, the purchase amount, the payment data, and The predicted sub-score and weight corresponding to the number of clicks are used to calculate the predicted score of the user to purchase the product to be recommended again.
  • the weight of the frequency of interest, the purchase amount, the payment data, and the number of clicks in calculating the predicted score may be set according to specific needs.
  • the weight ratio of the frequency of interest, the purchase amount, the payment data, and the number of clicks is set. It is 5:4:5:6. Since the predicted score of the embodiment is in units of a percentage system, the weight corresponding to the frequency of interest is 0.25, the weight corresponding to the purchase amount is 0.2, the weight corresponding to the payment data is 0.25, and the weight corresponding to the number of clicks is 0.3.
  • the predictor score corresponding to the frequency of interest is denoted as A
  • the predictor score corresponding to the purchase amount is denoted as B
  • the predictor score corresponding to the payout data is denoted as C
  • the predictor score corresponding to the click count is denoted as D
  • the operation data includes the frequency of attention of the user to the product in the application, the purchase amount of the product in the purchase application, the payment data corresponding to the purchased product, and the click count of the product to be recommended.
  • the frequency of attention is the number of days the user operates the product in the application;
  • the purchase amount of the product in the purchase application is the sum of the total amount of products purchased by the user in the application;
  • the payment data includes the total number of paymentes of the user and the number of times the payment is not made on time;
  • the number of clicks is the user The number of days to click on the content related to the product to be recommended in the app.
  • the frequency of interest and the number of clicks in order to reduce the amount of calculation, it may be set to acquire only the frequency of interest and the number of clicks for a fixed period of time, for example, it may be set to acquire only the frequency of interest from the current time and half a year. hit count.
  • the frequency of interest and the number of clicks are calculated in units of days, that is, regardless of the number of times the user operates the application on the same day, the frequency of interest is only recorded once, and the user clicks on the same day to be recommended. The number of times the product is related to the content, the number of clicks is only recorded once.
  • the unit of frequency of interest and number of clicks may be set to hours, or set to a unit of calculation of the user's operating frequency.
  • the preset score may be set according to specific needs.
  • the scores involved are in a percentage system, for example, the preset score may be set to 60 points, 65 points, etc., in other embodiments, The score may also be excluded from the percentage system.
  • each operation data has a corresponding preset rule, and the preset rules of different operation data are different.
  • the corresponding operation is calculated by the preset rule corresponding to the operation data.
  • the predicted sub-score of the data is obtained by predicting the score based on the predicted sub-score.
  • the product recommendation method further includes:
  • step d when the login operation of the application corresponding to the product to be recommended is detected, the user clicks on the product in the application.
  • Step e Obtain operation data of the user operating the product in the application according to the click operation, and store the operation data.
  • the application platform of the product to be recommended is an application corresponding to the merchant, that is, the application corresponding to the product to be recommended is installed in the product recommendation device.
  • the login operation of the application corresponding to the product to be recommended is detected, the user clicks on the product in the application, and the operation data of the product in the application is obtained according to the click operation, and the operation data is stored.
  • the user clicks on the product in the application the time when the click operation is detected is recorded, and the time is stored together with the corresponding operation data.
  • the step of calculating the predicted sub-score corresponding to the payment data according to the preset rule corresponding to the payment data based on the payment data includes:
  • Step f calculating a difference between the total number of payment in the payment data and the number of times the payment is not made on time.
  • Step g calculating a predicted sub-score corresponding to the payment data according to the difference value and the total number of payment times.
  • N1 represents the frequency of interest for half a year;
  • the predicted sub-score A 83.25 corresponding to the frequency of interest (in the present embodiment, the value corresponding to the predicted sub-score retains two decimal places).
  • N2 represents the purchase amount of the product purchased by the user in the application, and the unit is the yuan;
  • the value corresponding to the predicted sub-score retains two decimal places).
  • N3 represents the number of clicks in a half year period;
  • the value corresponding to the predicted sub-score retains two decimal places).
  • T1, T2, and T3 may be the same or different.
  • the operation data of the user who has successfully purchased the product to be recommended is acquired according to the triggering instruction; and the user is further purchased according to the operation data.
  • a predicted score of the recommended product if the predicted score is greater than the preset score, recommending the product to be recommended to the user. Calculating the predicted score of the user to purchase the product to be recommended again according to the operation data of the user, determining whether to recommend the product to be recommended to the user according to the predicted score, improving the purchase rate of the product to be recommended, and avoiding recommending the product to be recommended Users who have a small chance of purchase may cause user confusion; and for products that need to be renewed, the renewal rate of the renewed product is increased.
  • the product recommendation method further includes:
  • Step S40 Acquire a product of interest of the user, and determine a similarity between the product of interest and the product to be recommended.
  • Step S20 includes:
  • Step S21 Calculate, according to the similarity and the operation data, a predicted score that the user purchases the product to be recommended again.
  • the similarity between the product of interest and the product to be recommended is calculated according to the main factors considered by the user at the time of purchasing the product. If the product to be recommended is a wealth management product, the similarity between the product of interest and the product to be recommended can be calculated from four factors: financial period, risk level, product type and profit rate.
  • the similarity between the product of interest and the product to be recommended is calculated, and the predicted sub-score corresponding to the operation data is determined, the similarity and the weight corresponding to each operation data are determined, according to the similarity, the predicted sub-score and the weight corresponding to each operation data. Calculate the predicted score of the user who purchased the product to be recommended again.
  • E represents the similarity between the product of interest and the product to be recommended
  • a0 represents the weight corresponding to the frequency of interest
  • b0 represents The weight corresponding to the purchase amount
  • c0 represents the weight corresponding to the payment data
  • d0 represents the weight corresponding to the number of clicks
  • a0 The ratio between b0, c0, d0 and e0 can be set according to specific needs.
  • the similarity is used as a calculation factor for calculating the predicted score.
  • the similarity may also be used as the calculation of the frequency of interest, the purchase amount, the payment data, or the number of clicks corresponding to the predicted score. the weight of.
  • the similarity may be set as a calculation factor of the prediction score when the similarity is greater than or equal to the preset similarity; when the similarity is less than the preset similarity, the similarity is not used as a calculation factor of the predicted score.
  • the preset similarity can be set according to specific needs. For example, in the embodiment, the preset similarity can be set to 50%.
  • step S40 includes:
  • step h the user's product of interest is obtained, and the financial period, risk level, product type, and profit rate of the product of interest are obtained.
  • Step i comparing a financial period, a risk level, a product type, and a profit rate of the product of interest with a financial period, a risk level, a product type, and a profit rate of the product to be recommended, respectively, determining the product of interest and the The similarity between the products to be recommended.
  • the financial cycle, risk level, product type and profit rate of the user's attention product are obtained, and the financial management cycle, risk degree, product type and profit rate of the product are respectively treated and treated. Compare the financial period, risk level, product type and profitability of the recommended products to determine the similarity between the product of interest and the product to be recommended.
  • the similarity W M*m1+N*n1+P*p1+Q*q1.
  • M is the similarity score of the financial cycle
  • N is the similarity score of the risk degree
  • P is the similarity score of the product type
  • Q is the similarity score of the yield
  • m1 is the similarity between the financial product and the product to be recommended.
  • Weight n1 is the weight of the degree of risk in calculating the similarity between the product of interest and the product to be recommended
  • p1 is the weight of the product type in calculating the similarity between the product of interest and the product to be recommended
  • q1 is the rate of return in calculating the product of interest
  • m1:n1:p1:q1 6:4:5:5
  • the ratio between m1, n1, p1, and q1 can be set to be different from 6:4:5: The ratio of 5.
  • the financial cycle similarity score is obtained according to the difference between the attention product and the corresponding level of the financial period of the product to be recommended.
  • the corresponding level of the financial period is: the current period is recorded as 0 level; the financial period Y ⁇ 3, recorded as level 1; 3 ⁇ Y ⁇ 6, recorded as level 2; 6 ⁇ Y ⁇ 12, recorded as level 3; 12 ⁇ Y ⁇ 36, recorded as 4; 36 ⁇ Y ⁇ 60, recorded as 5; 60 ⁇ Y, recorded as 6.
  • the risk degree similarity score is obtained based on the difference between the level of concern for the product and the degree of risk of the product to be recommended.
  • the level of risk corresponds to: low risk is recorded as level 1; low risk is recorded as level 2; medium risk is recorded as level 3; medium to high risk is recorded as level 4; high risk is recorded as level 5;
  • the risk degree similarity score is divided into 100 points.
  • the rate of return similarity score is 100 points, calculated according to the annual rate of return.
  • the specific numerical values involved may be set according to specific needs, and are not limited to the values described above.
  • step S30 includes:
  • Step S31 If the predicted score is greater than the preset score, it is detected whether the predicted score is within a discount score corresponding to the preferential policy.
  • Step S32 If the predicted score is within the discount score range, recommend the product to be recommended to the user, and send a preferential policy for purchasing the product to be recommended to the user.
  • the predicted score is greater than the preset score, it is detected whether the predicted score is within the discount score corresponding to the preferential policy.
  • the predicted score is within the range of the preferential score corresponding to the preferential policy, the product to be recommended is recommended to the user, and the preferential policy for purchasing the product to be recommended is sent to the user.
  • the preferential policies and the preferential points corresponding to the preferential policies may be set according to specific needs, and are not limited in the embodiments of the present application. If the predicted score is not within the discount score, only the product to be recommended is recommended to the user.
  • each wealth management product has the lowest basic rate of return.
  • the basic rate of return of the product to be recommended is 3.5%, it can be set within a different range of predicted scores, corresponding to an increase in the rate of return. For example, when 80 ⁇ S ⁇ 85, the rate of return is equal to 3.55%; when 85 ⁇ S ⁇ 90, the rate of return is equal to 3.60%; when 90 ⁇ S ⁇ 95, the rate of return is equal to 3.65%; when 95 ⁇ S ⁇ 100 The yield is equal to 3.70%.
  • the preferential policy when the user reaches the preferential policy condition, when the product to be recommended is recommended to the user, the preferential policy is also sent to the user, so as to further improve the purchase rate of the user to purchase the recommended product, and improve the continuation.
  • the renewal rate of the product when the user reaches the preferential policy condition, when the product to be recommended is recommended to the user, the preferential policy is also sent to the user, so as to further improve the purchase rate of the user to purchase the recommended product, and improve the continuation. The renewal rate of the product.
  • the embodiment of the present application further provides a product recommendation device, where the product recommendation device includes:
  • the obtaining module is configured to: when detecting a triggering instruction for recommending the product to be recommended, obtain, according to the triggering instruction, operation data of the user who has successfully purchased the product to be recommended.
  • a calculation module configured to calculate, according to the operation data, a predicted score that the user purchases the product to be recommended again;
  • a recommendation module configured to recommend the product to be recommended to the user if the predicted score is greater than a preset score.
  • the acquiring module is further configured to acquire the product of interest of the user, and determine a similarity between the product of interest and the product to be recommended;
  • the calculation module is further configured to calculate, according to the similarity and the operation data, a predicted score that the user purchases the product to be recommended again.
  • the obtaining module includes:
  • An obtaining unit configured to acquire a product of interest of the user, and obtain a financial period, a risk level, a product type, and a profit rate of the product of interest;
  • a determining unit configured to compare a financial period, a risk level, a product type, and a profit rate of the product of interest with a financial period, a risk level, a product type, and a profit rate of the product to be recommended, respectively, to determine the product of interest and The similarity between the products to be recommended.
  • the product recommendation device further includes:
  • the detecting module is configured to detect, when the login operation of the application corresponding to the product to be recommended is logged in, the user clicks on the product in the application;
  • the obtaining module is further configured to acquire operation data of the user operating the product in the application according to the click operation, and store the operation data.
  • the operation data includes a frequency of attention of the user to the product in the application, a purchase amount of the product purchased in the application, payment data corresponding to the purchased product, and a click count of clicking the product to be recommended.
  • calculation module includes:
  • a first calculating unit configured to calculate, according to the frequency of interest, the purchase amount, the payment data, and the number of clicks, the predicted sub-score corresponding to the frequency of interest, the purchase amount, the payment data, and the number of clicks according to the corresponding preset rule;
  • a determining unit configured to determine the weight of the frequency of interest, the purchase amount, the payment data, and the number of clicks
  • the first calculating unit is further configured to calculate, according to the predicted sub-score and the weight, a predicted score that the user purchases the product to be recommended again;
  • the weight corresponding to the frequency of interest is 0.25, the weight corresponding to the purchase amount is 0.2, the weight corresponding to the payment data is 0.25, and the weight corresponding to the number of clicks is 0.3, if the frequency of interest is corresponding to
  • the calculating module is further configured to calculate a difference between the total number of payment in the payment data and the number of times that the payment is not paid; and calculate a predicted sub-score corresponding to the payment data according to the difference and the total number of payment.
  • the recommendation module includes:
  • a detecting unit configured to detect, if the predicted score is greater than the preset score, whether the predicted score is within a discount score corresponding to the preferential policy
  • a recommendation unit configured to: if the predicted score is within the discount score range, recommend the product to be recommended to the user, and send a preferential policy for purchasing the product to be recommended to the user.
  • each embodiment of the product recommendation device is substantially the same as the embodiments of the product recommendation method described above, and details are not described herein again.
  • the embodiment of the present application further provides a computer readable storage medium, where the product recommendation program is stored, and the product recommendation program is implemented by the processor to implement the step of the product recommendation method.
  • the above-mentioned storage medium may be a read only memory, a magnetic disk or an optical disk or the like.
  • the foregoing embodiment method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be through hardware, but in many cases, the former is better.
  • Implementation Based on such understanding, the technical solution of the present application, which is essential or contributes to the prior art, may be embodied in the form of a software product stored in a storage medium (such as ROM/RAM, disk,
  • the optical disc includes a number of instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the methods described in various embodiments of the present application.

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Abstract

一种产品推荐方法、装置、设备以及计算机可读存储介质，该方法包括步骤：当侦测到推荐待推荐产品的触发指令时，根据所述触发指令获取已成功购买所述待推荐产品用户的操作数据（S10）；根据所述操作数据计算所述用户再次购买所述待推荐产品的预测分数（S20）；若所述预测分数大于预设分数，则将所述待推荐产品推荐给所述用户（S30）。该方法实现了根据用户的操作数据计算用户再次购买待推荐产品的预测分数，根据预测分数来决定是否将待推荐产品推荐给用户，提高了待推荐产品的购买率；且对于需要续期的产品来说，提高了续期产品的续期率。

Description

产品推荐方法、装置、设备以及计算机可读存储介质
本申请要求于2017年6月20日提交中国专利局、申请号为201710474485.1、发明名称为“产品推荐方法、设备以及计算机可读存储介质”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
本申请涉及互联网技术领域，尤其涉及一种产品推荐方法、装置、设备以及计算机可读存储介质。
背景技术
在互联网技术迅速发展的今天，各种APP（Application，应用程序）软件经常会向用户推荐各种产品，以通过推荐产品，来提高产品的销售率。然而现有的产品推荐一般都是针对新用户，且一般都是通过广告的形式推荐产品，让用户主动去发现产品，购买产品。
当用户成功购买某个产品后，如成功购买保险产品或理财产品，用户就不会再去关注这个产品。对于需要续期的产品，当用户所购买的产品到期后，如果用户没有收到相应的推荐信息，也不会再去购买该产品，或者是忘记继续购买该产品，从而导致该产品的购买率下降，以及降低了需要续期产品的续期率。
发明内容
本申请的主要目的在于提供一种产品推荐方法、装置、设备以及计算机可读存储介质，旨在解决产品购买率低和续期产品续期率低的技术问题。
为实现上述目的，本申请提供一种产品推荐方法，所述产品推荐方法包括步骤：
当侦测到推荐待推荐产品的触发指令时，根据所述触发指令获取已成功购买所述待推荐产品用户的操作数据；
根据所述操作数据计算所述用户再次购买所述待推荐产品的预测分数；
若所述预测分数大于预设分数，则将所述待推荐产品推荐给所述用户。
此外，为实现上述目的，本申请还提供一种产品推荐装置，所述产品推荐装置包括：
获取模块，用于当侦测到推荐待推荐产品的触发指令时，根据所述触发指令获取已成功购买所述待推荐产品用户的操作数据；
计算模块，用于根据所述操作数据计算所述用户再次购买所述待推荐产品的预测分数；
推荐模块，用于若所述预测分数大于预设分数，则将所述待推荐产品推荐给所述用户。
此外，为实现上述目的，本申请还提供一种产品推荐设备，所述产品推荐设备包括存储器、处理器和存储在所述存储器上并可在所述处理器上运行的产品推荐程序，所述产品推荐程序被所述处理器执行时实现如上所述的产品推荐方法的步骤。
此外，为实现上述目的，本申请还提供一种计算机可读存储介质，所述计算机可读存储介质上存储有产品推荐程序，所述产品推荐程序被处理器执行时实现如上所述的产品推荐方法的步骤。
本申请通过当侦测到推荐待推荐产品的触发指令时，根据所述触发指令获取已成功购买所述待推荐产品用户的操作数据；根据所述操作数据计算所述用户再次购买所述待推荐产品的预测分数；若所述预测分数大于预设分数，则将所述待推荐产品推荐给所述用户。实现了根据用户的操作数据计算用户再次购买待推荐产品的预测分数，根据预测分数来决定是否将待推荐产品推荐给用户，提高了待推荐产品的购买率；且对于需要续期的产品来说，提高了续期产品的续期率。
附图说明
图1是本申请实施例方案涉及的硬件运行环境的设备结构示意图；
图2为本申请产品推荐方法第一实施例的流程示意图；
图3为本申请产品推荐方法第二实施例的流程示意图；
图4为本申请实施例中若所述预测分数大于预设分数，则将所述待推荐产品推荐给所述用户的一种流程示意图。
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。
如图1所示，图1是本申请实施例方案涉及的硬件运行环境的设备结构示意图。
本申请实施例产品推荐设备可以是PC，也可以是智能手机、平板电脑、电子书阅读器、MP3（Moving Picture Experts Group Audio Layer III，动态影像专家压缩标准音频层面3）播放器、MP4（Moving Picture Experts Group Audio Layer IV，动态影像专家压缩标准音频层面4）播放器、便携计算机等的可移动式终端设备。
如图1所示，该产品推荐设备可以包括：处理器1001，例如CPU，网络接口1004，用户接口1003，存储器1005，通信总线1002。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），可选用户接口1003还可以包括标准的有线接口、无线接口。网络接口1004可选的可以包括标准的有线接口、无线接口（如WI-FI接口）。存储器1005可以是高速RAM存储器，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。
可选地，产品推荐设备还可以包括摄像头、RF（Radio Frequency，射频）电路，传感器、音频电路、WiFi模块等等。
本领域技术人员可以理解，图1中示出的产品推荐设备结构并不构成对终端的限定，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。
如图1所示，作为一种计算机存储介质的存储器1005中可以包括操作系统以及产品推荐程序。其中，操作系统是管理和控制产品推荐设备硬件和软件资源的程序，支持产品推荐程序以及其它软件和/或程序的运行。
在图1所示的产品推荐设备中，网络接口1004主要用于连接用户所持终端，与用户所持终端进行数据通信；用户接口1003主要用于接收获取指令等。而处理器1001可以用于调用存储器1005中存储的产品推荐程序，并执行以下产品推荐方法的步骤。
本申请产品推荐设备具体实施方式与下述产品推荐方法各实施例基本相同，在此不再赘述。
基于上述的硬件结构，提出产品推荐方法的各个实施例。
参照图2，图2为本申请产品推荐方法第一实施例的流程示意图。
在本实施例中，提供了产品推荐方法的实施例，需要说明的是，虽然在流程图中示出了逻辑顺序，但是在某些情况下，可以以不同于此处的顺序执行所示出或描述的步骤。
所述产品推荐方法包括：
步骤S10，当侦测到推荐待推荐产品的触发指令时，根据所述触发指令获取已成功购买所述待推荐产品用户的操作数据。
当产品推荐设备侦测到推荐待推荐产品的触发指令时，根据触发指令获取已成功购买待推荐产品用户的操作数据。具体地，当产品推荐设备侦测到触发指令时，产品推荐设备的处理器1001根据触发指令从存储器1005中获取已成功购买待推荐产品用户的操作数据。操作数据包括但不限于用户对待推荐产品所在应用中各个产品的关注频率、购买该应用中各个产品的购买金额、与所购买产品对应的缴费数据和点击待推荐产品的点击次数。
在本申请实施例中，触发指令可由产品推荐设备自动触发，也可以由工作人员手动触发。当该触发指令由产品推荐设备自动触发时，可在产品推荐设备中设置一个定时任务（如可设置在每天定时触发该触发指令，或者在间隔一定时间段后触发该触发指令），当达到定时任务的条件时，产品推荐设备自动触发该触发指令。进一步地，在本实施例中，已成功购买待推荐产品表明用户已购买该待推荐产品，且已缴纳与待推荐产品对应的费用。
步骤S20，根据所述操作数据计算所述用户再次购买所述待推荐产品的预测分数。
步骤S30，若所述预测分数大于预设分数，则将所述待推荐产品推荐给所述用户。
当获取到用户的操作数据后，根据操作数据计算用户再次购买待推荐产品的预测分数，并判断预测分数是否大于预设分数。当预测分数大于预设分数时，将待推荐产品按照预设方式推荐给该用户；当预测分数小于或者等于预设分数时，不将待推荐产品推荐给该用户。
进一步地，步骤S20还可以包括：
步骤a，基于所述关注频率、购买金额、缴费数据和点击次数，分别按照对应的预设规则计算所述关注频率、购买金额、缴费数据和点击次数对应的预测子分数。
步骤b，确定所述关注频率、购买金额、缴费数据和点击次数的权重。
步骤c，根据所述预测子分数和所述权重计算所述用户再次购买所述待推荐产品的预测分数。
其中，所述关注频率对应的权重为0.25，所述购买金额对应的权重为0.2，所述缴费数据对应的权重为0.25，所述点击次数对应的权重为0.3，若将所述关注频率对应的预测子分数记为A，所述购买金额对应的预测子分数记为B，所述缴费数据对应的预测子分数记为C，所述点击次数对应的预测子分数记为D，所述预测分数记为S，则所述预测分数S=A*0.25+B*0.2+C*0.25+D*0.3。
进一步地，当获取到关注频率、购买金额、缴费数据和点击次数时，分别按照关注频率对应的预设规则计算关注频率的预测子分数，按照购买金额对应的预设规则计算购买金额对应的预测子分数，按照缴费数据对应的预设规则计算缴费数据对应的预测子分数，按照点击次数对应的预设规则计算点击次数对应的预测子分数。
当得到关注频率、购买金额、缴费数据和点击次数对应的预测子分数后，确定关注频率、购买金额、缴费数据和点击次数在计算预测分数中的权重，根据关注频率、购买金额、缴费数据和点击次数对应的预测子分数和权重计算用户再次购买待推荐产品的预测分数。
需要说明的是，关注频率、购买金额、缴费数据和点击次数在计算预测分数中的权重可根据具体需要而设置，在本实施例中，关注频率、购买金额、缴费数据和点击次数的权重比例为5:4:5:6。由于本实施例的预测分数是以百分制为单位的，因此，关注频率对应的权重为0.25，购买金额对应的权重为0.2，缴费数据对应的权重为0.25，点击次数对应的权重为0.3。若将关注频率对应的预测子分数记为A，购买金额对应的预测子分数记为B，缴费数据对应的预测子分数记为C，点击次数对应的预测子分数记为D，预测分数记为S，则预测分数S=A*0.25+B*0.2+C*0.25+D*0.3。
在本实施例中，操作数据包括用户对应用中产品的关注频率、购买应用中产品的购买金额、与所购买产品对应的缴费数据和点击待推荐产品的点击次数。关注频率为用户操作应用中产品的天数；购买应用中产品的购买金额为用户在该应用中所购买所有产品的金额总和；缴费数据包括用户的总缴费次数和未按时缴费次数；点击次数为用户在应用中点击与待推荐产品相关内容的天数。需要说明的是，在获取关注频率和点击次数过程中，为了减少计算量，可设置为只获取固定时间段的关注频率和点击次数，如可设置为只获取从当前时间起半年的关注频率和点击次数。在本实施例中，关注频率和点击次数都是以天为单位进行计算，即不管在同一天用户操作应用中产品次数的多少，关注频率只记为一次，也不管用户在同一天点击待推荐产品相关内容的次数的多少，点击次数也只记为一次。在其它实施例中，可将关注频率和点击次数的单位设置为小时，或者设置为以用户的操作频次为计算单位。
需要说明的是，预设分数可根据具体需要而设置，在本实施例中，所涉及的分数采用百分制，如预设分数可设置为60分，65分等，在其它实施例中，所涉及的分数也可以不采用百分制。预设方式可为一种或者多种，预设方式包括但不限于以短信、邮件和微信。在本实施例中，每一操作数据都有对应的预设规则，不同操作数据的预设规则是不一样的，在计算预测分数过程中，通过操作数据所对应的预设规则计算出对应操作数据的预测子分数，在根据预测子分数得到预测分数。
进一步地，所述产品推荐方法还包括：
步骤d，当侦测到登录购买待推荐产品对应应用的登录操作时，侦测所述用户对所述应用中产品的点击操作。
步骤e，根据所述点击操作获取所述用户操作所述应用中产品的操作数据，并存储所述操作数据。
进一步地，在本实施例中，待推荐产品的应用平台为对应商家的应用，即产品推荐设备中安装有与待推荐产品对应的应用。当侦测到登录购买待推荐产品对应应用的登录操作时，侦测用户对应用中产品的点击操作，并根据该点击操作获取用户对应用中产品的操作数据，存储该操作数据。在侦测到用户对应用中产品的点击操作时，记录侦测到该点击操作的时间，并将该时间与对应的操作数据一起存储。
进一步地，所述基于所述缴费数据，按照与所述缴费数据对应的预设规则计算所述缴费数据对应的预测子分数的步骤包括：
步骤f，计算所述缴费数据中总缴费次数与未按时缴费次数的差值。
步骤g，根据所述差值和所述总缴费次数计算所述缴费数据对应的预测子分数。
进一步地，基于缴费数据，按照与缴费数据对应的预设规则计算缴费数据对应的预测子分数的具体过程为：计算缴费数据中总缴费次数与未按时缴费次数的差值，根据该差值和总缴费次数计算缴费数据对应的预测子分数C。若将该差值记为c1，总缴费次数记为c2，则预测子分数C=c1/c2*c3+c4，在本实施例中，为了保证预测子分数是以百分制的形式表示，c3=c4=50。但在其他实施例中，c3和c4可以设置为其它值，且c3和c4的值可以相同，也可以不同。
进一步地，关注频率对应的预设规则为:关注频率n1<a1时，A=A1；当a1≤n1<a2时，A= A1+（n1- a1-1）*T1/（a2- a1）；当n1≥a2时，A=100。n1表示半年时间内的关注频率；T1为计算关注频率对应预测子分数的相关系数，为了保证预测子分数是以百分制的形式表示，T1的值应小于50，在本实施例中，T1=49.88。如当a1=10，a2=100，A1=50，n1=69时，关注频率对应的预测子分数A=83.25（在本实施例，预测子分数对应的值保留两位小数点）。
购买金额对应的预设规则为：购买金额n2≤b1时，B=B1；当b1<n2<b2时，B= B1+（n2- b1）*T2/（b2-b1）；当n2≥b2时，B=100。n2表示用户购买应用中产品的购买金额，单位为元；T2为计算购买金额对应预测子分数的相关系数，为了保证预测子分数是以百分制的形式表示，T2的值应小于50，在本实施例中，T2=49.88。如当b1=1000，b2=500000，B1=50，n2=50000时，购买金额对应的预测子分数B=50+（50000-1000）*49.88/（500000-1000）=54.99（在本实施例，预测子分数对应的值保留两位小数点）。
点击次数对应的预设规则为：点击次数 n3≤d1时，D=D1；当d1<n3<d2时，D= D1+（n3- d1）*T3/（d2-d1）；当n3≥d2时，D=100。n3表示半年时间内的点击次数；T3为计算点击次数对应预测子分数的相关系数，为了保证预测子分数是以百分制的形式表示，T3的值应小于50，在本实施例中，T3=49.88。如当d1=5，d2=15，D1=50，n3=12时，点击次数对应的预测子分数D=50+（12-5）*49.88/（15-5）=84.92（在本实施例，预测子分数对应的值保留两位小数点）。
需要说明的是，在本申请实施例中，T1、 T2和 T3对应的值可以相同，也可以不同。
本实施例通过当侦测到推荐待推荐产品的触发指令时，根据所述触发指令获取已成功购买所述待推荐产品用户的操作数据；根据所述操作数据计算所述用户再次购买所述待推荐产品的预测分数；若所述预测分数大于预设分数，则将所述待推荐产品推荐给所述用户。实现了根据用户的操作数据计算用户再次购买待推荐产品的预测分数，根据预测分数来决定是否将待推荐产品推荐给用户，提高了待推荐产品的购买率，同时避免了将待推荐产品推荐给购买几率小的用户，造成用户困扰的情况出现；且对于需要续期的产品来说，提高了续期产品的续期率。
进一步地，提出本申请产品推荐方法第二实施例。
所述产品推荐方法第二实施例与所述产品推荐方法第一实施例的区别在于，参照图3，产品推荐方法还包括：
步骤S40，获取所述用户的关注产品，确定所述关注产品与所述待推荐产品之间的相似度。
步骤S20包括：
步骤S21，根据所述相似度和所述操作数据计算所述用户再次购买所述待推荐产品的预测分数。
获取用户的关注产品，确定关注产品与待推荐产品之间的相似度，并根据相似度和所获取的操作数据计算用户再次购买待推荐产品的预测分数。具体地，在计算关注产品和待推荐产品之间的相似度时，根据用户在购买产品时所考虑的主要因素计算关注产品和待推荐产品之间的相似度。如待推荐产品为理财产品时，可从理财周期、风险程度、产品类型和收益率四个因素去计算关注产品和待推荐产品之间的相似度。
当计算到关注产品和待推荐产品之间的相似度、以及操作数据对应的预测子分数后，确定相似度和各个操作数据对应的权重，根据相似度、各个操作数据对应的预测子分数和权重计算用户再次购买待推荐产品的预测分数。如可设置为S=A*a0+B*b0+C*c0+D*d0+E*e0，E表示关注产品和待推荐产品之间的相似度，a0表示关注频率对应的权重，b0表示购买金额对应的权重，c0表示缴费数据对应的权重，d0表示点击次数对应的权重，e0表示相似度对应的权重。可以理解的是，a0+b0+c0+d0+e0=1。在本实施例中，a0、 b0、c0、d0和e0之间的比例可根据具体需要而设置。
进一步地，在本实施例中，是将相似度作为计算预测分数的一个计算因子，在其它实施例中，也可将相似度作为计算关注频率、购买金额、缴费数据或点击次数对应预测子分数的权重。
进一步地，可设置为当相似度大于或者等于预设相似度时，才将相似度作为预测分数的计算因子；当相似度小于预设相似度时，不将相似度作为预测分数的计算因子。预设相似度可根据具体需要而设置，如在本实施例中，预设相似度可设置为50%。
当所述关注产品和所述待推荐产品为理财产品时，所述步骤S40包括：
步骤h，获取所述用户的关注产品，以及获取所述关注产品的理财周期、风险程度、产品类型和收益率。
步骤i，将所述关注产品的理财周期、风险程度、产品类型和收益率分别与所述待推荐产品的理财周期、风险程度、产品类型和收益率进行对比，确定所述关注产品和所述待推荐产品之间的相似度。
进一步地，当关注产品和待推荐产品为理财产品时，获取用户关注产品的理财周期、风险程度、产品类型和收益率，将关注产品的理财周期、风险程度、产品类型和收益率分别与待推荐产品的理财周期、风险程度、产品类型和收益率进行对比，确定关注产品和待推荐产品之间的相似度。
具体地，在本实施例中，相似度W=M*m1+N*n1+P*p1+Q*q1。M为理财周期相似度分数，N为风险程度相似度分数，P为产品类型相似度分数，Q为收益率相似度分数，m1为理财周期在计算关注产品和待推荐产品之间的相似度的权重，n1为风险程度在计算关注产品和待推荐产品之间的相似度的权重，p1为产品类型在计算关注产品和待推荐产品之间的相似度的权重，q1为收益率在计算关注产品和待推荐产品之间的相似度的权重。在本实施例中，m1：n1：p1：q1=6:4:5:5，在其它实施例中，m1、n1、p1和q1之间的比值可设置为不同于6:4:5:5的比值。
在本实施例中，理财周期相似度分数为根据关注产品和待推荐产品理财周期对应等级的差级所得。理财周期对应的等级为：活期记为0级；理财周期Y<3，记为1级；3<Y≤6，记为2级；6<Y≤12，记为3级；12<Y≤36，记为4级；36<Y≤60，记为5级；60<Y，记为6级。理财周期Y以月份为单位；理财周期相似度分数总分为100分，关注产品和待推荐产品之间的理财周期每相差一个等级，理财周期相似度分数减5分。如当关注产品和待推荐产品之间的理财周期相差三个等级时，M=100-3*5=85。
风险程度相似度分数根据关注产品和待推荐产品风险程度对应等级的差级所得。风险程度对应的等级为：低风险记为1级；中低风险记为2级；中风险记为3级；中高风险记为4级；高风险记为5级。风险程度相似度分数总分为100分，关注产品和待推荐产品之间的风险程度每相差一个等级，理财周期相似度分数减5分。如当关注产品和待推荐产品之间的风险程度相差四个等级时，风险程度相似度分数N=100-5*4=80。
产品类型相似度分数可设置为，当关注产品和待推荐产品之间的类型相同时，产品类型相似度分数P=100，当关注产品和待推荐产品之间的类型不同时，产品类型相似度分数P=90。
收益率相似度分数满分是100分，按照年收益率百分数进行计算，关注产品和待推荐产品之间的年收益率每相差0.1%，收益率相似度分数减1分。如当关注产品和待推荐产品之间的年收益率相差1.1%时，收益率相似度分数Q=100-11=89。
需要说明的是，在计算理财周期、风险程度、产品类型和收益率对应的相似度分数过程中，所涉及的具体数值可根据具体需要而设置，并不限制于上述所描述的数值。
本实施例通过根据用户关注产品和待推荐产品之间的相似度和操作数据计算用户再次购买待推荐产品的预测分数，提高了预测用户再次购买待推荐产品的准确率。
进一步地，提出本申请产品推荐方法第三实施例。
所述产品推荐方法第三实施例与所述产品推荐方法第一实施例的区别在于，参照图4，步骤S30包括：
步骤S31，若所述预测分数大于所述预设分数，则检测所述预测分数是否在优惠政策对应的优惠分数范围内。
步骤S32，若所述预测分数在所述优惠分数范围内，则将所述待推荐产品推荐给所述用户，以及将购买所述待推荐产品的优惠政策发送给所述用户。
当预测分数大于预设分数时，检测预测分数是否在优惠政策对应的优惠分数范围内。当预测分数在优惠政策对应的优惠分数范围内时，将待推荐产品推荐给该用户，同时将购买待推荐产品的优惠政策发送给用户。优惠政策以及优惠政策对应的优惠分数可根据具体需要而设置，在本申请实施例中不做限制。若预测分数不在优惠分数范围内，则只将该待推荐产品推荐给该用户。
如可设置为当预测分数大于或者等于80分时（优惠分数范围为80至100分），用户可以享受购买待推荐产品的优惠政策。如待推荐产品为理财产品时，每一个理财产品都有最低的基本收益率。当待推荐产品的基本收益率为3.5%时，可设置在不同的预测分数范围内，对应提高收益率。如当80≤S<85时，收益率等于3.55%；当85≤S<90时，收益率等于3.60%；当90≤S<95时，收益率等于3.65%；当95≤S<100时，收益率等于3.70%。
本实施例通过设置优惠政策，当用户达到优惠政策条件时，在将待推荐产品推荐给用户时，将优惠政策也发送给用户，以进一步提高用户购买待推荐产品的购买率，以及提高了续期产品的续期率。
此外，本申请实施例还提出一种产品推荐装置，所述产品推荐装置包括：
获取模块，用于当侦测到推荐待推荐产品的触发指令时，根据所述触发指令获取已成功购买所述待推荐产品用户的操作数据。
计算模块，用于根据所述操作数据计算所述用户再次购买所述待推荐产品的预测分数；
推荐模块，用于若所述预测分数大于预设分数，则将所述待推荐产品推荐给所述用户。
进一步地，所述获取模块还用于获取所述用户的关注产品，确定所述关注产品与所述待推荐产品之间的相似度；
所述计算模块还用于根据所述相似度和所述操作数据计算所述用户再次购买所述待推荐产品的预测分数。
进一步地，当所述关注产品和所述待推荐产品为理财产品时，所述获取模块包括：
获取单元，用于获取所述用户的关注产品，以及获取所述关注产品的理财周期、风险程度、产品类型和收益率；
确定单元，用于将所述关注产品的理财周期、风险程度、产品类型和收益率分别与所述待推荐产品的理财周期、风险程度、产品类型和收益率进行对比，确定所述关注产品和所述待推荐产品之间的相似度。
进一步地，所述产品推荐装置还包括：
侦测模块，用于当侦测到登录购买待推荐产品对应应用的登录操作时，侦测所述用户对所述应用中产品的点击操作；
所述获取模块还用于根据所述点击操作获取所述用户操作所述应用中产品的操作数据，并存储所述操作数据。
进一步地，所述操作数据包括所述用户对所述应用中产品的关注频率、购买所述应用中产品的购买金额、与所购买产品对应的缴费数据和点击所述待推荐产品的点击次数。
进一步地，所述计算模块包括：
第一计算单元，用于基于所述关注频率、购买金额、缴费数据和点击次数，分别按照对应的预设规则计算所述关注频率、购买金额、缴费数据和点击次数对应的预测子分数；
确定单元，用于确定所述关注频率、购买金额、缴费数据和点击次数的权重；
所述第一计算单元还用于根据所述预测子分数和所述权重计算所述用户再次购买所述待推荐产品的预测分数；
其中，所述关注频率对应的权重为0.25，所述购买金额对应的权重为0.2，所述缴费数据对应的权重为0.25，所述点击次数对应的权重为0.3，若将所述关注频率对应的预测子分数记为A，所述购买金额对应的预测子分数记为B，所述缴费数据对应的预测子分数记为C，所述点击次数对应的预测子分数记为D，所述预测分数记为S，则所述预测分数S=A*0.25+B*0.2+C*0.25+D*0.3。
进一步地，所述计算模块还用于计算所述缴费数据中总缴费次数与未按时缴费次数的差值；根据所述差值和所述总缴费次数计算所述缴费数据对应的预测子分数。
进一步地，所述推荐模块包括：
检测单元，用于若所述预测分数大于所述预设分数，则检测所述预测分数是否在优惠政策对应的优惠分数范围内；
推荐单元，用于若所述预测分数在所述优惠分数范围内，则将所述待推荐产品推荐给所述用户，以及将购买所述待推荐产品的优惠政策发送给所述用户。
需要说明的是，产品推荐装置的各个实施例与上述产品推荐方法的各实施例基本相同，在此不再详细赘述。
此外，本申请实施例还提出一种计算机可读存储介质，所述计算机可读存储介质上存储有产品推荐程序，所述产品推荐程序被处理器执行时实现产品推荐方法的步骤。
本申请计算机可读存储介质具体实施方式与上述产品推荐方法各实施例基本相同，在此不再赘述。
需要说明的是，本领域普通技术人员可以理解实现上述实施例的全部或部分步骤可以通过硬件来完成，也可以通过程序来指令相关的硬件完成，所述的程序可以存储于一种计算机可读存储介质中，上述提到的存储介质可以是只读存储器，磁盘或光盘等。
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、物品或者装置不仅包括那些要素，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、物品或者装置所固有的要素。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、物品或者装置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. 一种产品推荐方法，其特征在于，所述产品推荐方法包括以下步骤：
    当侦测到推荐待推荐产品的触发指令时，根据所述触发指令获取已成功购买所述待推荐产品用户的操作数据；
    根据所述操作数据计算所述用户再次购买所述待推荐产品的预测分数；
    若所述预测分数大于预设分数，则将所述待推荐产品推荐给所述用户。
  2. 如权利要求1所述的产品推荐方法，其特征在于，所述根据所述操作数据计算所述用户再次购买所述待推荐产品的预测分数的步骤之前，还包括：
    获取所述用户的关注产品，确定所述关注产品与所述待推荐产品之间的相似度；
    所述根据所述操作数据计算所述用户再次购买所述待推荐产品的预测分数的步骤包括：
    根据所述相似度和所述操作数据计算所述用户再次购买所述待推荐产品的预测分数。
  3. 如权利要求2所述的产品推荐方法，其特征在于，当所述关注产品和所述待推荐产品为理财产品时，所述获取所述用户的关注产品，确定所述关注产品与所述待推荐产品之间的相似度的步骤包括：
    获取所述用户的关注产品，以及获取所述关注产品的理财周期、风险程度、产品类型和收益率；
    将所述关注产品的理财周期、风险程度、产品类型和收益率分别与所述待推荐产品的理财周期、风险程度、产品类型和收益率进行对比，确定所述关注产品和所述待推荐产品之间的相似度。
  4. 如权利要求1所述的产品推荐方法，其特征在于，所述当侦测到推荐待推荐产品的触发指令时，根据所述触发指令获取已成功购买所述待推荐产品用户的操作数据的步骤之前，还包括：
    当侦测到登录购买待推荐产品对应应用的登录操作时，侦测所述用户对所述应用中产品的点击操作；
    根据所述点击操作获取所述用户操作所述应用中产品的操作数据，并存储所述操作数据。
  5. 如权利要求4所述的产品推荐方法，其特征在于，所述操作数据包括所述用户对所述应用中产品的关注频率、购买所述应用中产品的购买金额、与所购买产品对应的缴费数据和点击所述待推荐产品的点击次数。
  6. 如权利要求5所述的产品推荐方法，其特征在于，所述根据所述操作数据计算所述用户再次购买所述待推荐产品的预测分数的步骤包括：
    基于所述关注频率、购买金额、缴费数据和点击次数，分别按照对应的预设规则计算所述关注频率、购买金额、缴费数据和点击次数对应的预测子分数；
    确定所述关注频率、购买金额、缴费数据和点击次数的权重；
    根据所述预测子分数和所述权重计算所述用户再次购买所述待推荐产品的预测分数；
    其中，所述关注频率对应的权重为0.25，所述购买金额对应的权重为0.2，所述缴费数据对应的权重为0.25，所述点击次数对应的权重为0.3，若将所述关注频率对应的预测子分数记为A，所述购买金额对应的预测子分数记为B，所述缴费数据对应的预测子分数记为C，所述点击次数对应的预测子分数记为D，所述预测分数记为S，则所述预测分数S=A*0.25+B*0.2+C*0.25+D*0.3。
  7. 如权利要求6所述的产品推荐方法，其特征在于，所述基于所述缴费数据，按照与所述缴费数据对应的预设规则计算所述缴费数据对应的预测子分数的步骤包括：
    计算所述缴费数据中总缴费次数与未按时缴费次数的差值；
    根据所述差值和所述总缴费次数计算所述缴费数据对应的预测子分数。
  8. 如权利要求1所述的产品推荐方法，其特征在于，所述若所述预测分数大于预设分数，则将所述待推荐产品推荐给所述用户的步骤包括：
    若所述预测分数大于所述预设分数，则检测所述预测分数是否在优惠政策对应的优惠分数范围内；
    若所述预测分数在所述优惠分数范围内，则将所述待推荐产品推荐给所述用户，以及将购买所述待推荐产品的优惠政策发送给所述用户。
  9. 如权利要求2所述的产品推荐方法，其特征在于，所述若所述预测分数大于预设分数，则将所述待推荐产品推荐给所述用户的步骤包括：
    若所述预测分数大于所述预设分数，则检测所述预测分数是否在优惠政策对应的优惠分数范围内；
    若所述预测分数在所述优惠分数范围内，则将所述待推荐产品推荐给所述用户，以及将购买所述待推荐产品的优惠政策发送给所述用户。
  10. 如权利要求3所述的产品推荐方法，其特征在于，所述若所述预测分数大于预设分数，则将所述待推荐产品推荐给所述用户的步骤包括：
    若所述预测分数大于所述预设分数，则检测所述预测分数是否在优惠政策对应的优惠分数范围内；
    若所述预测分数在所述优惠分数范围内，则将所述待推荐产品推荐给所述用户，以及将购买所述待推荐产品的优惠政策发送给所述用户。
  11. 如权利要求4所述的产品推荐方法，其特征在于，所述若所述预测分数大于预设分数，则将所述待推荐产品推荐给所述用户的步骤包括：
    若所述预测分数大于所述预设分数，则检测所述预测分数是否在优惠政策对应的优惠分数范围内；
    若所述预测分数在所述优惠分数范围内，则将所述待推荐产品推荐给所述用户，以及将购买所述待推荐产品的优惠政策发送给所述用户。
  12. 如权利要求5所述的产品推荐方法，其特征在于，所述若所述预测分数大于预设分数，则将所述待推荐产品推荐给所述用户的步骤包括：
    若所述预测分数大于所述预设分数，则检测所述预测分数是否在优惠政策对应的优惠分数范围内；
    若所述预测分数在所述优惠分数范围内，则将所述待推荐产品推荐给所述用户，以及将购买所述待推荐产品的优惠政策发送给所述用户。
  13. 如权利要求6所述的产品推荐方法，其特征在于，所述若所述预测分数大于预设分数，则将所述待推荐产品推荐给所述用户的步骤包括：
    若所述预测分数大于所述预设分数，则检测所述预测分数是否在优惠政策对应的优惠分数范围内；
    若所述预测分数在所述优惠分数范围内，则将所述待推荐产品推荐给所述用户，以及将购买所述待推荐产品的优惠政策发送给所述用户。
  14. 如权利要求7所述的产品推荐方法，其特征在于，所述若所述预测分数大于预设分数，则将所述待推荐产品推荐给所述用户的步骤包括：
    若所述预测分数大于所述预设分数，则检测所述预测分数是否在优惠政策对应的优惠分数范围内；
    若所述预测分数在所述优惠分数范围内，则将所述待推荐产品推荐给所述用户，以及将购买所述待推荐产品的优惠政策发送给所述用户。
  15. 一种产品推荐装置，其特征在于，所述产品推荐装置包括：
    获取模块，用于当侦测到推荐待推荐产品的触发指令时，根据所述触发指令获取已成功购买所述待推荐产品用户的操作数据；
    计算模块，用于根据所述操作数据计算所述用户再次购买所述待推荐产品的预测分数；
    推荐模块，用于若所述预测分数大于预设分数，则将所述待推荐产品推荐给所述用户。
  16. 如权利要求15所述的产品推荐装置，其特征在于，所述获取模块还用于获取所述用户的关注产品，确定所述关注产品与所述待推荐产品之间的相似度；
    所述计算模块还用于根据所述相似度和所述操作数据计算所述用户再次购买所述待推荐产品的预测分数。
  17. 如权利要求16所述的产品推荐装置，其特征在于，当所述关注产品和所述待推荐产品为理财产品时，所述获取模块包括：
    获取单元，用于获取所述用户的关注产品，以及获取所述关注产品的理财周期、风险程度、产品类型和收益率；
    确定单元，用于将所述关注产品的理财周期、风险程度、产品类型和收益率分别与所述待推荐产品的理财周期、风险程度、产品类型和收益率进行对比，确定所述关注产品和所述待推荐产品之间的相似度。
  18. 如权利要求15所述的产品推荐装置，其特征在于，所述产品推荐装置还包括：
    侦测模块，用于当侦测到登录购买待推荐产品对应应用的登录操作时，侦测所述用户对所述应用中产品的点击操作；
    所述获取模块还用于根据所述点击操作获取所述用户操作所述应用中产品的操作数据，并存储所述操作数据。
  19. 一种产品推荐设备，其特征在于，所述产品推荐设备包括存储器、处理器和存储在所述存储器上并可在所述处理器上运行的产品推荐程序，所述产品推荐程序被所述处理器执行时实现如权利要求1所述的产品推荐方法的步骤。
  20. 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质上存储有产品推荐程序，所述产品推荐程序被处理器执行时实现如权利要求1所述的产品推荐方法的步骤。
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