Recommendation method for products of interest to users

A recommendation method and user technology, which is applied in the field of recommendation of products that users are interested in, can solve the problems of lack of adaptability, limitation of data learning and understanding of multi-pair sorting algorithm, lack of personalization, etc., achieve accurate recommendation effect and improve recommendation ability Effect

Active Publication Date: 2022-01-11
UNIV OF SCI & TECH OF CHINA
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  • Abstract
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  • Application Information

AI Technical Summary

Problems solved by technology

Such a sampling method has two disadvantages: first, it is a static sampling method that lacks adaptability; second, it is a global sampling method that lacks individuality
The above two shortcomings affect the recommendation ability of the multi-pair sorting algorithm and limit the learning and understanding of the multi-pair sorting algorithm for data.

Method used

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  • Recommendation method for products of interest to users
  • Recommendation method for products of interest to users
  • Recommendation method for products of interest to users

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Embodiment Construction

[0021] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0022] An embodiment of the present invention provides a method for recommending products of interest to users, which uses an adaptive sampling method to improve the recommendation ability of the multi-pair ranking algorithm, making the recommendation effect of the multi-pair ranking recommendation method more accurate.

[0023] Such as figure 1 As shown, the embodiment of the present invention provides a flowchart of a method for recommending item...

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Abstract

The invention discloses a method for recommending commodities that users are interested in. The method uses a user-commodity preference matrix learned from the historical consumption behavior of users and commodities by using a multi-pair ranking algorithm, and uses a matrix decomposition model to construct the user-commodity preference matrix. model, fully consider the hidden vector representation information of users and products, through the above adaptive sampling method, it can adaptively mine the products that each user is potentially interested in, so that the recommendation effect of the multi-pair ranking recommendation method is more accurate.

Description

technical field [0001] The invention relates to the fields of machine learning and recommendation systems, in particular to a method for recommending products of interest to users. Background technique [0002] Collaborative filtering algorithm is one of the most popular algorithms in recommender systems. Previous research has paid more attention to the collaborative filtering algorithm based on user rating data, but in more application scenarios in life, it is difficult for us to obtain user ratings on products. The historical records of a large number of users do not contain explicit rating information (such as the products purchased by the user, the URLs followed), and we call this type of data the user's implicit feedback. In recent years, scholars from various countries have gradually shifted their research attention from explicit feedback to implicit feedback recommendation. Different from the explicit feedback data, for each user, the implicit feedback data only con...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q30/06
Inventor 陈恩红刘淇于润龙程明月叶雨扬
Owner UNIV OF SCI & TECH OF CHINA
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