A Hybrid Personalized Recommendation Method Based on Dynamic Bayesian Network

Abstract:

With the rapid growth and wide application of electronic commerce, lots of information comes forth to people. However, our experiences and knowledge often do not enough to process the vast amount of information. The problem of obtaining useful information becomes more and more serious. To deal with the problem, the personalized service and recommender system play a more important role in many fields and collaborative filtering is one of the most successful technologies in recommender systems. However, with the tremendous growth in the amount of items and users, most collaborative filtering algorithms suffer from data sparsity which leads to inaccuracy of recommendation. Aiming at the problem of data sparsity, a hybrid personalized recommendation method based on dynamic Bayesian networks is presented. This method uses the dynamic Bayesian network technique to fill the vacant ratings at first, and then employs the user-based collaborative filtering to produce recommendations.

• Periodical: Advanced Materials Research (Volumes 268-270)
• Pages: 1082-1085
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.268-270.1082
• Online since: July 2011
• Authors: Yu Long Ying, Quan Ping Hua
• Keywords: Dynamic Bayesian Network, Hybrid Recommendation, Personalized Service, Recommender System

References

[1] Songjie Gong, An Efficient Collaborative Recommendation Algorithm Based on Item Clustering, Lecture notes in electrical engineering, Volume 72, pp: 381-387.

DOI: 10.1007/978-3-642-14350-2_48

[2] Herlocker, J. (2000). Understanding and Improving Automated Collaborative Filtering Systems. Ph.D. Thesis, Computer Science Dept., University of Minnesota.

[3] Songjie Gong, Employing User Attribute and Item Attribute to Enhance the Collaborative Filtering Recommendation, Journal of Software, Volume 4, Number 8, October 2009, pp: 883-890.

DOI: 10.4304/jsw.4.8.883-890

[4] L. H. Ungar and D. P. Foster. A Formal Statistical Approach to Collaborative Filtering. Proceedings of Conference on Automated Leading and Discovery (CONALD), (1998).

[5] B. Sarwar, G. Karypis, J. Konstan and J. Riedl, Recommender systems for large-scale e-commerce: Scalableneighborhood formation using clustering, Proceedings of the Fifth International Conference on Computer andInformation Technology, (2002).

[6] Songjie Gong, A Collaborative Filtering Recommendation Algorithm Based on User Clustering and Item Clustering, Journal of Software, Volume 5, Number 7, July 2010, pp: 745-752.

DOI: 10.4304/jsw.5.7.745-752

[7] Y-H. Chien and E.I. George. A bayesian model for collaborative filtering. In Proceedings of the Sev-enth International Workshop on Articial Intelli-gence and Statistics, San Francisco, 1999. MorganKaufmann.

[8] Jin, R., & Si, L. (2004). A bayesian approach toward active learning for collaborativefiltering. Proceedings of the 20th conference on Uncertainty in artificial intelligence(p.278–285). Banff, Canada: AUAI Press.

[9] Chickering D, Hecherman D. Efficient approximations for the marginal likelihood of Bayesian networks with hidden variables. Machine Learning, 1997, 29(2/3): 181~212.

[10] M. O. Conner and J. Herlocker. Clustering Items for Collaborative Filtering. In Proceedings of the ACM SIGIR Workshop on Recommender Systems, Berkeley, CA, August (1999).

[11] A. Kohrs and B. Merialdo. Clustering for Collaborative Filtering Applications. In Proceedings of CIMCA'99. IOS Press, (1999).

[12] Lee, WS. Online clustering for collaborative filtering. School of Computing Technical Report TRA8/00. (2000).