An Efficient Recommender System based on Collaborative Filtering


  • V. Anbarasu
  • X. Linda
  • S. Mahalakshmi Jeppiaar Engineering college


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.


Author Biography

S. Mahalakshmi, Jeppiaar Engineering college

Information Technology


Hao Ma, Irwin King, Senior Member, IEEE, and Michael Rung-Tsong Lyu, Fellow, IEEE “Mining Web Graphs for Recommendations†IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 24, NO. 6, JUNE 2012.

Sonia Ben Tunisia & Nancy “User Semantic Model for Hybrid Recommender Systems†Boyer KIWI Team, LORIA laboratory.

Z. Zheng, H. Ma, M. R. Lyu, et al., “QoS-aware Web service recommendation by Collaborative Filtering,†IEEE Trans. on Services Computing, vol. 4, no. 2, pp. 140-152, February 2011.

Song Jie Gong Zhejiang “A Collaborative Filtering Recommendation Algorithm Based on User Clustering and Item Clustering†Business Technology Institute, Ningbo 315012, China

Guibing Guo, Jie Zhang â€Leveraging Multiviews of Trust and Similarity to Enhance Clustering-based Recommender Systems†Neil Yorke-Smith_School of Computer Engineering, Nanyang Technological University, Lebanon; and University of Cambridge, UK fgguo1.




How to Cite

Anbarasu, V., Linda, X., & Mahalakshmi, S. (2015). An Efficient Recommender System based on Collaborative Filtering. Asian Journal of Applied Sciences, 3(1). Retrieved from