Intelligent Recommendation Based on Multiple Data Sources and Co-Clustering
WANG Rui-Qin1,2, KONG Fan-Sheng1
1.College of Computer Science and Technology, Zhejiang University, Hangzhou 3100272. School of Computer Science and Engineering, Wenzhou University, Wenzhou 325035
Abstract With the development of internet and e-commerce, intelligent recommendation system emerges as the time requires. Collaborative filtering (CF) is regarded as the most effective recommender technique, but it has some limitations such as sparsity, scalability and cold start problems. In this paper, a hybrid recommendation method is proposed to overcome the limitations of CF. Firstly, a smooth filling technique is used on rating matrix with multiple data sources to solve the sparsity problem. Next, co-clustering technique from both user and item aspects is adopted to improve the scalability and precision of the system. The experimental results demonstrate the proposed approach has higher recommend accuracy than traditional collaborative filtering, meanwhile its online recommendation speed is fast.
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2008, Vol. 21 
