An Efficient Recommender System based on Collaborative Filtering
Keywords:Clustering, Collaborative Filtering, Mashup
In general Big Data enterprise large-volume of complex, growing data sets with multiple, autonomous sources. The utmost underlying challenge for the Big Data applications is to explore the large volumes of data and extract useful information or knowledge for future actions. In view of this challenge, we propose a method called Clustering based Collaborative Filtering approach. It consists of two stages: clustering and Collaborative Filtering. Clustering is an initial step to separate big data into manageable parts. A cluster contains some similar services. In the second stage, a Collaborative Filtering algorithm is applied on one of the clusters. As the number of services in a cluster is much less than the total number of services, the computation time of collaborative filtering algorithm can be reduced significantly. Besides, since the ratings of similar services within a cluster are more relevant than that of dissimilar services, the recommendation accuracy based on user ratings may be enhanced.
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Sonia Ben Tunisia & Nancy â€œUser Semantic Model for Hybrid Recommender Systemsâ€ Boyer KIWI Team, LORIA laboratory.
Z. Zheng, H. Ma, M. R. Lyu, et al., â€œQoS-aware Web service recommendation by Collaborative Filtering,â€ IEEE Trans. on Services Computing, vol. 4, no. 2, pp. 140-152, February 2011.
Song Jie Gong Zhejiang â€œA Collaborative Filtering Recommendation Algorithm Based on User Clustering and Item Clusteringâ€ Business Technology Institute, Ningbo 315012, China
Guibing Guo, Jie Zhang â€Leveraging Multiviews of Trust and Similarity to Enhance Clustering-based Recommender Systemsâ€ Neil Yorke-Smith_School of Computer Engineering, Nanyang Technological University, Lebanon; and University of Cambridge, UK fgguo1.
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