A Personalized Hybrid Recommendation System Oriented to E-Commerce Mass Data in the Cloud

Personalized recommendation technology in E-commerce is widespread to solve the problem of product information overload. However, with the further growth of the number of E-commerce users and products, the original recommendation algorithms and systems will face several new challenges: (1) to model user's interests more accurately, (2) to provide more diverse recommendation modes, and (3) to support large-scale expansion. To address these challenges, from the actual demands of E-commerce applications (as Made-in-China website), a personalized hybrid recommendation system, which can support massive data set, is designed and implemented in this paper by using Cloud technology. Hereinto, the recommendation algorithms are designed based on a novel user interesting model for different scenarios, and the massive data parallel processing techniques in Cloud computing is utilized to realize the effective execution of recommendation algorithms. Finally, several experiments are presented to highlight the system performance.

[1]  Bhaskar Mehta,et al.  Attack resistant collaborative filtering , 2008, SIGIR '08.

[2]  Katia P. Sycara,et al.  WebMate: a personal agent for browsing and searching , 1998, AGENTS '98.

[3]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[4]  Jiahui Liu,et al.  Personalized news recommendation based on click behavior , 2010, IUI '10.

[5]  Hsinchun Chen,et al.  A Comparison of Collaborative-Filtering Recommendation Algorithms for E-commerce , 2007, IEEE Intelligent Systems.

[6]  Jun Wang,et al.  Unifying user-based and item-based collaborative filtering approaches by similarity fusion , 2006, SIGIR.

[7]  John Riedl,et al.  Application of Dimensionality Reduction in Recommender System - A Case Study , 2000 .

[8]  Hsinchun Chen,et al.  A comparison of collaborative-filtering algorithms for ecommerce , 2007 .

[9]  Haitao Li,et al.  A hybrid collaborative filtering recommendation mechanism for P2P networks , 2010, Future Gener. Comput. Syst..

[10]  Randy H. Katz,et al.  A view of cloud computing , 2010, CACM.

[11]  Yehuda Koren,et al.  Build your own music recommender by modeling internet radio streams , 2012, WWW.

[12]  Zuhua Jiang,et al.  Distributed recommender for peer-to-peer knowledge sharing , 2010, Inf. Sci..

[13]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[14]  Randy H. Katz,et al.  Above the Clouds: A Berkeley View of Cloud Computing , 2009 .

[15]  Philip S. Yu,et al.  Horting hatches an egg: a new graph-theoretic approach to collaborative filtering , 1999, KDD '99.

[16]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.