Computer Science ›› 2017, Vol. 44 ›› Issue (10): 182-186.doi: 10.11896/j.issn.1002-137X.2017.10.034

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Multi-criteria Recommendation Algorithm Based on Codebook-clustering and Factorization Machines

DING Yong-gang, LI Shi-jun, YU Wei and WANG Jun   

  • Online:2018-12-01 Published:2018-12-01

Abstract: The sparsity of user-item ratings is a common problem and the users who share similar preferences on multi-criteria cannot be found by only making use of a single overall rating to calculate the similarity of users in traditional collaborative filtering algorithm,which would affect the accuracy of recommendation.Multi-criteria recommendation algorithm tries to find users who share similar preferences on multi-criteria,but the problem of data sparsity become even worse owing to the high cost of rating.Aim at these problems,we proposed an algorithm which first obtains the information of rating style of users based on the idea of codebook-clustering,and then conducts co-clustering for users and items on each criteria.Finally,this algorithm makes recommendations by factorization machines(FMs) based on users,items,multi-criteria ratings and rating style.The experimental result shows that multi-criteria recommendation algorithm based on codebook-clustering and FMs is able to solve the problem of data sparsity to some extent,thus improving the accuracy of recommendation.

Key words: User preference,Multi-criteria ratings,Codebook-clustering,Factorization machines(FMs)

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