Computer Science ›› 2018, Vol. 45 ›› Issue (6A): 465-470, 486.

• Big Date & Date Mining • Previous Articles     Next Articles

TEFRCF:Collaborative Filtering Personalized Recommendation Algorithm Based on Tag
Entropy Feature Representation

HE Ming,YANG Peng,YAO Kai-sheng,ZHANG Jiu-ling   

  1. Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China
  • Online:2018-06-20 Published:2018-08-03

Abstract: Tags are served as an effective way for information classification and information retrieval at the age of Web2.0.Tag recommendation systems aim to provide personalized recommendation for users by using tag data.Theexi-sting tag-based recommendation methods tend to assign the popular tags and their corresponding items more larger weight in predicting users’ interest on the items,resulting in weight deviations,reducing the novelty of the results and being unable to fully reflect users’ personalized interest.In order to solve the problems above,the concept of tag entropy was defined to measure the uncertainty of tags,and the collaborative filtering personalized recommendation algorithm based on tags entropy feature representation was proposed.This method solves the problem of weight deviation by introducing tag entropy,and then the tripartite graphs are used to describe the relationship among users,tags and items.The representation of users and items is constructed based on tag entropy feature representation,and the similarity of items is calculated by the feature similarity measure method.Finally,the user preferences for items are predicted by the linear combination of tags behaviors and similarity of items,and then the recommended list is generated according to the rank of preferences.The experimental results on Last.fm show that the proposed algorithm can improve recommendation accuracy and novelty,and satisfy the requirement for users.

Key words: Collaborative filtering, Tag, Entropy, Recommendation systems

CLC Number: 

  • TP391
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