Computer Science ›› 2018, Vol. 45 ›› Issue (6A): 415-422.

• Big Date & Date Mining • Previous Articles     Next Articles

Collaborative Filtering Personalized Recommendation Based on Similarity of Tag Information Feature

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

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

Abstract: Tag recommendation systems are aimed to provide personalized recommendation using tag data for users.Previous tag based recommendation methods usually neglect the characteristics of users and items,and similarity mea-sures are unconsidered fully incorporating effectively both user similarity and item similarity,which leads to deviation of recommendation results.To address this issue,this paper proposed the collaborative filtering recommendation method of combining tag features and similarity for personalized recommendation.Two-dimensional matrix is used to define actions among user-tag and tag-item based on integrating information among users,tags and items.Tag features representation is constructed,and user similarity and item similarity are calculated by similarity measure method based on tag features.The user preferences for items are predicted by their tag behaviors and linear combination of similarity of users and items,and the recommended list is generated according to the rank of preferences.The experimental results on Last.fm show that the proposed method can improve recommendation accuracy and satisfy the requirement for users.

Key words: Collaborative filtering, Tag, Recommendation systems, Similarity computation

CLC Number: 

  • TP391
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