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Method for collaborative filtering recommendation based on interest changes and trust relations

A collaborative filtering recommendation and trust relationship technology, applied in the field of personalized recommendation system, can solve the problems of unsatisfactory recommendation accuracy and failure to consider interest deviation, etc., and achieve the effect of accurate recommendation results

Inactive Publication Date: 2017-04-19
HANGZHOU DIANZI UNIV
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AI Technical Summary

Problems solved by technology

The user-based collaborative filtering algorithm recommends items for the user that are similar to the user's interests, but the existing user-based collaborative filtering algorithm does not take into account the user's interest deviation over time and the influence of the user's trust network. resulting in unsatisfactory recommendation accuracy

Method used

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  • Method for collaborative filtering recommendation based on interest changes and trust relations
  • Method for collaborative filtering recommendation based on interest changes and trust relations
  • Method for collaborative filtering recommendation based on interest changes and trust relations

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Embodiment Construction

[0043] In order to describe the present invention more specifically, the recommended method of the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0044] Such as figure 1 As shown, the collaborative filtering recommendation algorithm based on interest changes and trust relationships includes the following steps:

[0045] (1) Fusion time decay function to calculate user interest similarity.

[0046] figure 2 Schematic matrix for user-item rating data, U 1 ,...,U 4 Indicates 4 different users, I 1 ,...,I 5 Indicates 5 different items, and the user rating has 5 levels, which are 1, 2, 3, 4, and 5 respectively. If the user rates an item, the rating level will be marked at the corresponding position.

[0047] The Pearson correlation coefficient measures the linear correlation between two variables. The formula for calculating the Pearson similarity between users U and V is as follows:

[0048] ...

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Abstract

The invention discloses an algorithm for collaborative filtering recommendation based on interest changes and trust relations. The algorithm mainly comprises the steps that (1) users' interest similarity degrees are computed by a time-fusion attenuation function; (2) a trust network of the users are established, and the users' trust degrees are computed; (3) the users' similarity degrees are computed in combination of the users' interest similarity degrees and the users' trust degree; and (4) a score of a target user aiming at a project is predicted. According to the invention, based on computation of the users' interest similarity degrees with application of the time-fusion attenuation function, network modeling is conducted for evaluation relations between the users and the project, and trust relations between the users are analyzed and excavated. Finally, the user's interest and trust relations are synthesized in the collaborative filtering recommendation. In this way, recommendation accuracy is enhanced.

Description

technical field [0001] The invention relates to a personalized recommendation system technology, in particular to a collaborative filtering recommendation method based on interest changes and trust relationships. Background technique [0002] The recommendation system emerged to solve the problem of information overload and is widely used in e-commerce, movies, videos, music reading, advertising and other fields. By analyzing a large number of user behavior logs, the recommendation system can display different personalized pages to different users, improving the click-through rate and conversion rate of the website. [0003] At present, the main recommendation strategies include: collaborative filtering recommendation, content-based recommendation, association rule-based recommendation and hybrid recommendation. Among them, the collaborative filtering recommendation algorithm is one of the most successful techniques, which has been deeply researched and widely used in acade...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
CPCG06F16/9535
Inventor 徐小良刘智捷
Owner HANGZHOU DIANZI UNIV
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