JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2018, Vol. 48 ›› Issue (3): 96-102.doi: 10.6040/j.issn.1672-3961.0.2017.404

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An additive co-clustering for recommendation of integrating social network

DU Xixi, LIU Huafeng, JING Liping*   

  1. Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China
  • Received:2017-08-23 Online:2018-06-20 Published:2017-08-23

Abstract: In order to solve the problem of user cold start problem and improve the prediction accuracy of recommendation algorithm, an additive co-clustering recommendation model combining social networks(SN-ACCRec)was proposed, which integrated user social relations into user clustering of rating matrix. According to the social relations theory analysis of users, user blocks was divided with the idea of fuzzy C means clustering, and a co-clustering result was acquired by clusters items on rating matrix according to k-means algorithm. The general and specific categories was gotten by generating the user and item additive co-clustering results in an iterative method and pedict the missing values. The model was evaluated using ten fold cross validation method, and experimental results showed that this model could reduce the average absolute error(MAE)and the root mean square error(RMSE), which also showed a better recommendation performance in the cold start users.

Key words: personalized recommendation, social networks, co-clustering

CLC Number: 

  • TP311
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