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Collaborative filtering method for personalized recommendation fusion content and behavior

A collaborative filtering and behavioral technology, applied in data processing applications, character and pattern recognition, instruments, etc., can solve problems such as dependence and data sparsity

Active Publication Date: 2018-06-26
TONGJI UNIV
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  • Description
  • Claims
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AI Technical Summary

Problems solved by technology

However, relying too much on user ratings will face serious data sparsity problems

Method used

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  • Collaborative filtering method for personalized recommendation fusion content and behavior
  • Collaborative filtering method for personalized recommendation fusion content and behavior
  • Collaborative filtering method for personalized recommendation fusion content and behavior

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Experimental program
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Embodiment

[0078] Such as figure 1 As shown, a collaborative filtering method for fusion content and behavior for personalized recommendation, the method includes the following steps:

[0079] (1) Feature input: feature input includes item-attribute matrix representing item content and user-item browsing matrix, user-item collection matrix, user-item purchase matrix, item evaluation label matrix and user-item rating matrix representing user behavior ;

[0080] (2) Content-based item clustering: combine item-attribute matrix and item evaluation label matrix, calculate item similarity, and cluster items;

[0081] (3) Rating prediction and feature filling: For the unrated items in the user-item rating matrix, find the nearest neighbor items in the clusters with high similarity to the unrated items, and according to the ratings of the nearest neighbor items, unrated The item performs rating prediction and populates the user-item rating matrix;

[0082] (4) Behavior-based user clustering: ...

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Abstract

The invention relates to a collaborative filtering method for personalized recommendation fusion content and behavior. The method comprises the following steps of, (1) characteristic input, includinga project-attribute matrix representing a project content and a user behavior matrix representing user behaviors; (2) content-based project clustering for calculating the similarity of projects and clustering the projects; (3) score prediction and feature filling including carrying out score prediction on the non-scoring projects, and filling a user-project scoring matrix; (4) behavior-based userclustering including clustering users according to a project clustering result and a user-project scoring matrix; (5) score predication and project recommendation including determining the clusteringcluster where the target users are located, finding a nearest neighbor user set, performing score prediction on the non-scoring projects of the target users, and finally recommending the first N projects with the highest prediction scores to the target users. Compared with the prior art, the collaborative filtering method effectively solves the problems of data sparsity and cold start, and ensureshigh recommendation efficiency.

Description

technical field [0001] The invention relates to a collaborative filtering method in personalized recommendation, in particular to a collaborative filtering method for fusing content and behavior. Background technique [0002] Since Resnick and Varian proposed the concept of recommender systems in the 1990s, recommender systems have attracted extensive attention from academia and industry due to their important theoretical and application values, and have developed into an independent research field. In academia, there are many famous research institutions abroad, which have produced a large number of breakthrough academic research and academic papers. In the industry, practical recommendation systems serve various fields such as Internet news, e-commerce, and social networking. Major Internet companies at home and abroad also have teams specializing in recommendation systems. [0003] The common sorting and popular recommendation of each site is also a form of recommendatio...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06Q30/06
CPCG06Q30/0631G06F18/23213
Inventor 马云龙刘敏袁菡殷蓉
Owner TONGJI UNIV
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