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Collaborative filtering recommendation method for integrating preference relationship and trust relationship

A technology of collaborative filtering recommendation and trust relationship, which is applied in the field of collaborative filtering recommendation that integrates preference trust relationship, and can solve problems such as inaccurate recommendation results and sparse user rating data

Inactive Publication Date: 2012-11-14
JILIN UNIV
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AI Technical Summary

Problems solved by technology

[0008] Aiming at the problem of inaccurate recommendation results generated by the recommendation system due to sparse user rating data, the purpose of the present invention is to provide a collaborative filtering recommendation method that integrates preference trust relationships

Method used

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  • Collaborative filtering recommendation method for integrating preference relationship and trust relationship
  • Collaborative filtering recommendation method for integrating preference relationship and trust relationship
  • Collaborative filtering recommendation method for integrating preference relationship and trust relationship

Examples

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example 1

[0052] Example 1 The present invention's experimental results on the real data set EPINIONS

[0053] Epinions.com is an American Online service website where users can rate items and add other users to their trust list. The Epinions experimental dataset contains 234,311 pieces of rating information on 11,643 items from 5,178 users and 155,023 pieces of trust relationship information between these users. Example 1 applies the method of the present invention to this data set for test verification, and selects the performance of two index evaluation methods, one is mean absolute error (MAE), and the other is root mean square error (RMSE). There are ways to compare. The six comparison methods are user-based collaborative filtering method (user-based CF), item-based collaborative filtering method (item-based CF), user average rating method (mean rating of users), item average rating method (mean rating of users), trust based collaborative filtering method (trust based CF), reliab...

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Abstract

A collaborative filtering recommendation method for integrating preference relationship and trust relationship includes the following steps: digging preference relationship between users according to a project grading datum of users and building a preference relationship network; integrating preference relationship and trust relationship and building a preference and trust relationship network; positioning similar neighbors of a target user in the Markov random walk method and on the basis of the preference and trust relationship network; and forecasting grading values of corresponding projects for the target user on the basis of the grading value of the similar neighbors on a certain project. By adopting the method, a recommendation system can forecast grades of the projects made by the users efficiently in a novel mode. Compared with the prior art, the collaborative filtering recommendation method has the advantages of being simple, easy to achieve and capable of generating accurate grading forecasting values; and being convenient to select due to the fact that the method has only one parameter and recommended results are insensitive to the parameter.

Description

technical field [0001] The invention belongs to the field of information retrieval, and in particular relates to a collaborative filtering recommendation method integrating preference and trust relationships. Background technique [0002] The recommendation system can help users easily find the most interesting content (such as news, books, movies and music, etc.) from massive information, and it is one of the main ways to solve the information overload of the Internet, and has been widely used in e-commerce. [0003] At present, a variety of recommendation methods have been proposed, such as collaborative filtering recommendation method, content-based recommendation method and hybrid recommendation method. Among them, the collaborative filtering algorithm has been successfully applied in large-scale commercial recommendation systems because of its easy understanding and simple implementation. The basic principle of the user-based collaborative filtering method is to find o...

Claims

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Application Information

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IPC IPC(8): G06F17/30
Inventor 杨博赵鹏飞赵学华刘大有
Owner JILIN UNIV
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