CN103325052A - Commodity recommendation method based on multidimensional user consumption propensity modeling - Google Patents
Commodity recommendation method based on multidimensional user consumption propensity modeling Download PDFInfo
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Abstract
The invention discloses a commodity recommendation method based on multidimensional user consumption propensity modeling. According to the method, through analyzing multidimensional information such as a user browsing history, a consumption record and a user behavior record, the real consumption propensity of the user is speculated, and the method has a very important actual application value for the improvement of a personalized commodity recommendation effect and the conversion of the effect into an actual purchase behavior. Firstly, a multidimensional user consumption propensity model for the field of electronic commerce can be obtained, and a foundation is laid for the following personalized recommendation. Secondly, combined with a user interest classification and a cyclical consumption dimension propensity analysis, a personalized commodity recommendation effect in accordance with the consumption propensity of a client can be provided to the client. The method result can be widely applied to an electronic commerce recommendation application system.
Description
Technical field
The present invention relates to e-commerce field, in particular a kind of Method of Commodity Recommendation based on the modeling of various dimensions customer consumption tendency.Carry out targetedly commercial product recommending by analysis user consumption propensity, promote and recommend accuracy and conversion ratio.
Background technology
The less consideration customer consumption of existing way of recommendation tendency thus when recommending comparatively blindly, client's its purpose when consumption is comparatively clear and definite in fact, for example the higher client of income its pay close attention to and actual purchase mostly be greatly high-end commodity, if to recommendation low side commodity tend to form invalid recommendation.
Therefore, there is defective in prior art, needs to improve.
Summary of the invention
The present invention is directed to the e-commerce field characteristics, propose a kind of Method of Commodity Recommendation based on the modeling of various dimensions customer consumption tendency.The one, can obtain the various dimensions customer consumption tendency model in Electronic Commerce field, for follow-up personalized recommendation lays the foundation; The 2nd, consume the dimension trend analysis in conjunction with user interest classification and periodicity, can provide the Extraordinary commercial product recommending that meets its consumption propensity result to the client.The method achievement can be widely used in the ecommerce exemplary application system.
A kind of Method of Commodity Recommendation based on the modeling of various dimensions customer consumption tendency may further comprise the steps:
Step 1: obtain the interested classification of user according to browsing of user and purchaser record;
Step 2: below each classification, set up various dimensions tendency vector model, determine the dimension of passing judgment on;
Step 3: record is browsed in the purchase according to the user, calculates the user to the tendency degree of each dimension attribute component of commodity:
To each dimension, calculate the number percent p that the user buys certain attribute component of commodity association
1, calculate simultaneously the dispersion D of the commodity of interconnection vector in each dimension; Formula is as follows:
P wherein
1For belonging to the commodity ratio of each attribute component,
Be average, m is the component number; Dispersion shows the user to the tendency degree of this dimension vector, be worth larger, then the tendency more obvious:
Step 4: to the interested classification of each user, dispersion to each dimension of obtaining in the step 3 is carried out descending sort, choose simultaneously maximum in each a dimension vector as the propensity value of user in this dimension, finally form user's consumption propensity model;
Step 5: periodically consume the dimension tendency: at first can determine whether the user has the tendency of periodically buying according to user's purchaser record, if do not have then confirm by other users' purchaser record; If periodically consumption propensity is arranged, then to calculate consumption cycle, real-time update is estimated next consumption time, selecting accurately, be lead referral opportunity; For the commodity of aperiodicity consumption, then associated articles or annex are recommended in random selection;
Step 6: according to customer consumption tendency model with whether periodically consume and select accurately opportunity to the high commodity of lead referral user degree of tendency.
Adopt such scheme, the present invention browses the various dimensions information such as record, consumer record and user behavior record by analysis user, infer user's true consumption propensity, have very important actual application value to promoting the personalized commercial recommendation effect and it effectively being converted into actual purchase behavior.
Embodiment
Below in conjunction with specific embodiment, the present invention is described in detail.
1, obtains the interested classification of user according to the browsing with purchaser record of user of electric quotient system system record
2, below each classification, set up various dimensions tendency vector model, determine the dimension of judge, as:
1) price: the commodity price class that the user buys, such as price dimension=(height, in, low)
2) discount: the user is to the tendency of discounting commodity, such as discount dimension=(sensitivity, insensitive)
3) brand: the tendency of user on the commodity brand is selected, such as brand dimension=(domestic, Japan and Korea S, America and Europe)
4) seller: the tendency of user in commodity seller selection, such as seller's dimension=(platform self-operation, third party's operation)
5) periodically consumption: the user is consumption propensity periodically, such as periodically consuming dimension=(being, no)
6) modes of payments: the user is to the tendency of the modes of payments, such as modes of payments dimension=(cash on delivery, Credit Card Payments, on-line payment ...)
7) internal rating: the user is with reference to the tendency of internal rating, such as the internal rating dimension=(not grading, 1 star, 2 stars ...)
3, browse record according to user's purchase, calculate the user to the tendency degree of each dimension attribute component of commodity:
To each dimension, calculate the number percent that the user buys certain attribute component of commodity association, such as the price dimension, may obtain following value: high-end: 90%, middle-end: 10%.Calculate simultaneously the dispersion D of the commodity of interconnection vector in each dimension.Formula is as follows:
P wherein
1For belonging to the commodity ratio of each attribute component,
Be average, m is the component number.Dispersion shows the user to the tendency degree of this dimension vector, be worth larger, then the tendency more obvious.
4, to the interested classification of each user, the dispersion of step 3 kind of each dimension of obtaining is carried out descending sort, such as being distributed as of price dimension: 90%, 10%, 0; Being distributed as of brand dimension: 30%, 40%, 30%.Then the user is more obvious to the selection tendency of price, preferentially recommends according to the price tendency during recommendation.Choose simultaneously in each dimension a maximum vector as the propensity value of user in this dimension, such as the price dimension, 90% be high-end, 10% be middle-end, low side be 0, then price dimension user's tendency is high-end product.The final consumption propensity model that forms the user
5, periodically consume the dimension tendency: at first can determine that whether the user has the tendency of periodically buying, and can not confirm by other users' purchaser record if having according to user's purchaser record.If periodically consumption propensity is arranged, then to calculate consumption cycle, real-time update is estimated next consumption time, selecting accurately, be lead referral opportunity.For the commodity of aperiodicity consumption, can select at random to recommend associated articles or annex etc.
6, according to customer consumption tendency model and whether periodically consumption select accurately opportunity to the high commodity of lead referral user degree of tendency.
Should be understood that, for those of ordinary skills, can be improved according to the above description or conversion, and all these improvement and conversion all should belong to the protection domain of claims of the present invention.
Claims (1)
1. Method of Commodity Recommendation based on the modeling of various dimensions customer consumption tendency may further comprise the steps:
Step 1: obtain the interested classification of user according to browsing of user and purchaser record;
Step 2: below each classification, set up various dimensions tendency vector model, determine the dimension of passing judgment on:
Step 3: record is browsed in the purchase according to the user, calculates the user to the tendency degree of each dimension attribute component of commodity:
To each dimension, calculate the number percent p that the user buys certain attribute component of commodity association
1, calculate simultaneously the dispersion D of the commodity of interconnection vector in each dimension; Formula is as follows:
P wherein
1For belonging to the commodity ratio of each attribute component,
Be average, m is the component number; Dispersion shows the user to the tendency degree of this dimension vector, be worth larger, then the tendency more obvious;
Step 4: to the interested classification of each user, dispersion to each dimension of obtaining in the step 3 is carried out descending sort, choose simultaneously maximum in each a dimension vector as the propensity value of user in this dimension, finally form user's consumption propensity model;
Step 5: periodically consume the dimension tendency: at first can determine whether the user has the tendency of periodically buying according to user's purchaser record, if do not have then confirm by other users' purchaser record; If periodically consumption propensity is arranged, then to calculate consumption cycle, real-time update is estimated next consumption time, selecting accurately, be lead referral opportunity; For the commodity of aperiodicity consumption, then associated articles or annex are recommended in random selection;
Step 6: according to customer consumption tendency model with whether periodically consume and select accurately opportunity to the high commodity of lead referral user degree of tendency.
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| CN104281964A (en) * | 2014-09-29 | 2015-01-14 | 深圳市百科在线科技发展有限公司 | Clothing product recommendation aid decision making method and system based on real-time human model |
| CN104318344A (en) * | 2014-09-29 | 2015-01-28 | 深圳市百科在线科技发展有限公司 | Consumption characteristic-based product production assistant decision making method and system |
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| CN108182625A (en) * | 2017-12-28 | 2018-06-19 | 广州品唯软件有限公司 | A kind of electric business user Method of Commodity Recommendation and device |
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| CN109785007A (en) * | 2019-01-24 | 2019-05-21 | 博拉网络股份有限公司 | Electric business back-end data parser |
| CN110097369A (en) * | 2019-03-18 | 2019-08-06 | 深圳壹账通智能科技有限公司 | Transaction data processing method, device, electronic equipment and storage medium |
| CN110348964A (en) * | 2019-07-09 | 2019-10-18 | 葛晓滨 | It is a kind of based on the wisdom electronic commerce recommending method more perceived |
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| CN113656637A (en) * | 2021-07-26 | 2021-11-16 | 北京达佳互联信息技术有限公司 | Video recommendation method and device, electronic equipment and storage medium |
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Application publication date: 20130925 |