Issue No. 02 - June (2014 vol. 2)
Daqiang Zhang , School of Software Engineering, Tongji University, Shanghai, China
Ching-Hsien Hsu , Department of Computer Science, Chung Hua University, Hsinchu, Taiwan
Min Chen , Department of Computer Science, Huazhong University of Science and Technology, Wuhan, China
Quan Chen , Department of Computer Science, Shanghai Jiao Tong University, Shanghai, China
Naixue Xiong , School of Computer Science, Colorado Technical University, Colorado Springs, CO, USA
Jaime Lloret , Department of Communications, Polytechnic University of Valencia, Valencia, Spain
Social recommender systems leverage collaborative filtering (CF) to serve users with content that is of potential interesting to active users. A wide spectrum of CF schemes has been proposed. However, most of them cannot deal with the cold-start problem that denotes a situation that social media sites fail to draw recommendation for new items, users or both. In addition, they regard that all ratings equally contribute to the social media recommendation. This supposition is against the fact that low-level ratings contribute little to suggesting items that are likely to be of interest of users. To this end, we propose bi-clustering and fusion (BiFu)-a newly-fashioned scheme for the cold-start problem based on the BiFu techniques under a cloud computing setting. To identify the rating sources for recommendation, it introduces the concepts of popular items and frequent raters. To reduce the dimensionality of the rating matrix, BiFu leverages the bi-clustering technique. To overcome the data sparsity and rating diversity, it employs the smoothing and fusion technique. Finally, BiFu recommends social media contents from both item and user clusters. Experimental results show that BiFu significantly alleviates the cold-start problem in terms of accuracy and scalability.
Recommender systems, Social network services, Content management, Collaboration, Media
D. Zhang, C. Hsu, M. Chen, Q. Chen, N. Xiong and J. Lloret, "Cold-Start Recommendation Using Bi-Clustering and Fusion for Large-Scale Social Recommender Systems," in IEEE Transactions on Emerging Topics in Computing, vol. 2, no. 2, pp. 239-250, 2014.