A Trust-Based Collaborative Filtering Algorithm Using a User Preference Clustering

Nannan SUN, Yayun ZHUANG, Yayun ZHUANG, Shuqing NI

Abstract


Collaborative filtering is a widely adopted approach to recommendation, but sparse and high dimensional data are often barriers to providing high quality recommendations. Meanwhile, the traditional methods only utilize the information of the user-item rating matrix but ignore the trust relations between users, so their recommendation precision is often unsatisfactory. To address such issues, this paper constructs an user-preference matrix to reduce the data dimension and clusters the users by k-means clustering algorithm. Incorporating trust relationship, an improved similarity method is proposed to compute the similarity value. Then we find the nearest neighbor in the target user’s category according to the similarity; and predict the user’s prediction score by the nearest neighbor. At last we recommend the items with high prediction score to the user. This improved method has been tested via MovieLens 100K in order to make a comparison with the traditional techniques. The results have indicated that the proposed method can enhance performance of recommender systems.


Keywords


User preference clustering; Trust relationship; Collaborative filtering

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References


Abdul-Rahman, A., & Hailes, S. (2000). Supporting trust in virtual communities. In 3th Hawaii International Conference on System Sciences, Hawaii, USA.

Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734-749.

Bedi, P., & Sharma, R. (2012). Trust based recommender system using ant colony for trust computation. Expert Syst. Appl., 39, 1183-1190.

Birtolo, C., & Ronca, D. (2013). Advances in clustering collaborative filtering by means of fuzzy c-means and trust. Expert Syst. Appl., 40, 6997-7009.

Bojnordi, E., & Moradi, P. (2013). A novel collaborative filtering model based on combination of correlation method with matrix completion technique. Iran. J. Sci. Technol. Trans. Electr. Eng., 37, 93-100.

Breese, J., Heckerman, D., & Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence. Madison, USA: [s. n.].

Breese, J. S., Heckerman, D., & Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering (pp.43-52). In Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers Inc., Madison, Wisconsin.

Castro-Schez, J., & Miguel, R. (2011). A highly adaptive recommender system based on fuzzy logic for B2C ecommerce portals. Expert Systems with Applications, 38(3), 2441-2454.

DuBois, T., Golbeck, J., Kleint, J., & Srinivasan, A. (2009). Improving recommendation accuracy by clustering social networks with trust. In ACM RecSys Workshop Recommender Systems and the Social Web.

Golbeck, J. (2006). Generating predictive movie recommendations from trust in social networks. In 4th International Conference on Trust Management Pisa, Italy.

Golbeck, J., & Hendler, J. (2006). FilmTrust: Movie recommendations using trust in web-based social networks (pp.282-286). In IEEE Consumer Communications and Networking Conference.

Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004). Evaluating collaborative filtering recommender systems, ACM Trans. Inform. Syst., 22, 5-53.

Jamali, M., & Ester, M. (2010). A matrix factorization technique with trust propagation for recommendation in social networks. In ACM Conference on Recommender Systems.

Jamali, M., & M. Ester, M. (2009). Using a trust network to improve top-N recommendation (pp.181-188). In Proceedings of the Third ACM Conference on Recommender Systems, Pub- lishing, New York, New York, USA.

Javari, A., Gharibshah, J., & Jalili, M. (2014). Recommender systems based on collaborative filtering and resource allocation. Soc. Netw. Anal. Min., 4, 1-11.

Javari, A., & Jalili, M. (2014). Cluster-based collaborative filtering for sign prediction in social networks with positive and negative links. ACM Trans. Intell. Syst.Technol. (TIST) 5, 24.

Kelleher, J., & Bridge, D. (2003). Rectree centroid: An accurate, scalable collaborative recommender (pp.89-94). In Proceedings of the Fourteenth Irish Conference on Artificial Intelligence and Cognitive Science, Citeseer.

Kitisin, S., & Neuman, C. (2006). Reputation-based trust-aware recommender system (pp.1-7). In Securecomm and Workshops.

Liu, B., & Yuan, Z. (2010). Incorporating social networks and user opinions for collaborative recommendation: Local trust network based method (pp.53-56). In Proceedings of the Workshop on Context-aware Movie Recommendation, ACM, New York, USA.

Lü, L., Medo, M., Yeung, C. H., Zhang, Y.-C., Zhang, Z.-K., & Zhou, T. (2012). Recommender systems. Phys. Rep., 519, 1-49.

Massa, P., & Avesani, P. (2004). Trust-aware collaborative filtering for recommender systems. In Federated Int. Conf on the Move to Meaningful Internet.

Massa, P., & Bhattacharjee, B. (2004). Using trust in recommender systems: An experimental analysis (pp.221-235). In Proceedings of 2nd International Conference on Trust Managment, Oxford, England.

Massa, P., & Avesani, P. (2005). Controversial users demand local trust metrics: An experimental study on epinions.com community. In American Association for Artificial Intelligence (AAAI) Conference, San Francisco, USA.

Massa, P., & Avesani, P. (2007). Trust-aware recommender systems. In 2007 ACM Conference on Recommender Systems, Minneapolis, Minnesota, USA.

Navgaran, D. Z., Moradi, P., & Akhlaghian, F. (2013). Evolutionary based matrix factorization method for collaborative filtering systems (pp.1-5). In 2013 21st Iranian Conference on Electrical Engineering, ICEE, IEEE.

Park, S. T., & Pennock, D. M. (2007). Applying collaborative filtering techniques to movie search for better ranking and browsing (pp.550-559). Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press.

Ramezani, M., Moradi, P., & Akhlaghian, F. (2014). A pattern mining approach to enhance the accuracy of collaborative filtering in sparse data domains. Physica A, 408, 72-84.

Rashid, A. M., Lam, S. K., Karypis, G., & Riedl, J. (2006). Clustknn: A highly scalable hybrid model-& memory-based cf algorithm. In Proceedings of WebKDD, Citeseer, Philadelphia, PA.

Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., & Riedl, J. (1994). GroupLens: An open architecture for collaborative filtering of netnews (pp.175-186). In Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, ACM, Chapel Hill, North Carolina, USA.

Sarda, K., Gupta, P., Mukherjee, D., Padhy, S., & Saran, H. (2008). A distributed trust-based recommendation system on social network. In IEEE 10th Second Workshop Hot Topics in Web Systems and Technologies.

Shang, M.-S., Zhang, Z.-K., Zhou, T., & Zhang, Y.-C. (2010). Collaborative filtering with diffusion-based similarity on tripartite graphs. Physica A, 389, 1259-1264.

Sinde, S. K., & Kulkarni, U. (2012). Hybrid personalized recommender system using centering-bunching based clustering algorithm. Expert Systems with Applications, 39(1), 1381-1387.

Tsai, C. F., & Huang, C. H. (2012). Cluster, ensembles in collaborative filtering recommendation. Appl. Soft Comput, 12, 1417-1425.

Walter, F., Battiston, S., & Schweitzer, F. (2007). A model of a trust-based recommendation system on a social network, Auton. Agents Multi-Agent Syst., 16, 1573-7454.

Wang, J. H. (2014). Collaborative filtering recommendation lgorithm combining trust mechanism with user preferences. Chongqing University.

Xu, H. L., Wu, X., Li, X. D., & Yan, B. P. (2009). Comparison study of internet recommendation system. Journal of Software, (20), 350-362.

Yu, X., & Li, M. Q. (2010). Effective hybrid collaborative filtering model based on PCA-SOM. System Engineering-Theory & Practice, 30(10), 1850-1854.

Zhang, J., Peng, Q., Sun, S., & Liu, C. (2014). Collaborative filtering recommendation algorithm based on user preference derived from item domain features. Physica A, 396, 66-76.




DOI: http://dx.doi.org/10.3968/10046

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