A personalized commodity recommendation method and system

A product recommendation and product technology, applied in the field of recommendation, can solve problems such as deviation, labels that cannot completely cover product characteristics, and labels that cannot truly represent user interests, and achieve the effect of accurate product prediction

Active Publication Date: 2021-07-16
BEIJING UNIV OF POSTS & TELECOMM +1
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AI Technical Summary

Problems solved by technology

The problem with the method based on tag selection is that some users may not fill in the classification tags at the beginning of use, and may choose to "skip this step" or choose randomly, resulting in the selected tags not really representing the user's interests
And the product that the user is interested in may change over time, so there is a big difference between the originally selected interest tag and the current product of interest. For example, the product that a user is interested in in summer may be "sunscreen", but it changes in winter "Down jacket", if the recommendation is based on the user's original selection label, there will be a big deviation
In addition, due to the variety of product types, labels cannot completely cover all product characteristics

Method used

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  • A personalized commodity recommendation method and system
  • A personalized commodity recommendation method and system
  • A personalized commodity recommendation method and system

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Experimental program
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Effect test

Embodiment 1

[0048] Embodiment 1 of the present invention provides a personalized product recommendation method, the method includes steps S110-S140:

[0049] In step S110, historical behavior data of a plurality of users within a preset time period is obtained, and the first training samples are obtained after sorting according to predetermined rules.

[0050] The specific value of the preset time period and the number of users to be extracted can be set according to the actual situation. For example, if the time period is set to one month and the number of users to be extracted is Num, Num randomly selected users within one month will be extracted from all current data historical behavioral data. Since personalized recommendations are usually time-sensitive, for example, a user browsed "down jacket" half a year ago, but recently browsed "dress", if the user's browsing data half a year ago is still considered, the recommendation effect may be counterproductive. The value needs to be set ...

Embodiment 2

[0076] Embodiment 2 of the present invention also provides a personalized product recommendation system, including:

[0077] An acquisition module, configured to acquire historical behavior data of multiple users within a preset time period, and obtain the first training sample after sorting according to predetermined rules;

[0078] A calculation module, configured to use the sorted historical behavior data to calculate the impact factor corresponding to each commodity in the historical behavior data of each user as the second training sample based on the cosine similarity method;

[0079] The training module is used to train the first training sample and the second training sample as the training samples of the deep learning model to obtain the trained deep learning model;

[0080] The recommendation module is used to input the first training sample and the second training sample of the pre-recommended user into the trained deep learning model, output the product list predic...

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Abstract

The present invention discloses a personalized product recommendation method and system. The method includes: obtaining the historical behavior data of multiple users within a preset time period, and obtaining the first training sample after sorting according to predetermined rules; obtaining the influence factor based on the cosine similarity method as The second training sample; using the first training sample and the second training sample as the training samples of the deep learning model to train to obtain the trained deep learning model; outputting a list of commodities that the user is interested in predicted by the model. The invention effectively utilizes the timing information of commodities in the user's historical behavior, so that the commodities in the historical behavior have different weight values ​​according to the time sequence of their interactive behavior in the calculation of the recommendation system, and the commodity impact factor reflects the global characteristics of the commodity and The user's interest in the product can effectively increase the feature quantity obtained by the deep learning model, and effectively improve the personalized recommendation effect for cold-start users.

Description

technical field [0001] The invention relates to a personalized product recommendation method and system, which belong to the technical field of recommendation. Background technique [0002] With the popularization of the Internet and the rapid development of e-commerce, more and more users browse and purchase goods through e-commerce platforms. There are many kinds of commodities in the e-commerce platform, without the help of a personalized recommendation engine, it is difficult to accurately recommend the commodities that users are interested in to users. [0003] One of the mainstream methods of personalized recommendation methods used by current e-commerce platforms: collaborative filtering method, by looking for users with similar historical browsing or purchasing behaviors to the current user, recommending products that are of interest to users with similar behaviors to the current user. The personalized recommendation method based on collaborative filtering gives the...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q30/06
CPCG06Q30/0631
Inventor 张洪刚孙宇常剑徐彬高珊
Owner BEIJING UNIV OF POSTS & TELECOMM
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