Weighting Strategies for a Recommender System Using Item Clustering Based on Genres

[en] Recommender Systems are effective to identify items that could interest clients on e-commerce web sites or predict evaluations that people could give to items such as movies. In this context, clustering can be used to improve predictions or to reduce computational time. In this paper, we present a clustering approach based on item metadata informations. Evaluations are clustered according to item genre. As items can have several genres, evaluations can be placed in several clusters. Each cluster provides its own rating prediction and weighting strategies are then used to combine these results in one evaluation. Coupled with an existing collaborative filtering recommender system and applied on Yahoo! and MovieLens datasets, our method improves the MAE between 0.3 and 1.8%, and the RMSE between 4.7 and 9.8%.

Disciplines :

Computer science
Library & information sciences

Author, co-author :

Fremal, Sébastien ; Université de Mons > Faculté Polytechnique > Informatique, Logiciel et Intelligence artificielle

Lecron, Fabian ; Université de Mons > Faculté Polytechnique > Management de l'Innovation Technologique

Language :

English

Title :

Weighting Strategies for a Recommender System Using Item Clustering Based on Genres

Publication date :

25 January 2017

Journal title :

Expert Systems with Applications

ISSN :

0957-4174

Publisher :

Elsevier, Oxford, United Kingdom

Volume :

Pages :

105-113

Peer reviewed :

Peer Reviewed verified by ORBi

Research unit :

F113 - Management de l'Innovation Technologique
F114 - Informatique, Logiciel et Intelligence artificielle

Research institute :

R300 - Institut de Recherche en Technologies de l'Information et Sciences de l'Informatique

Available on ORBi UMONS :

since 06 January 2017

Statistics

Number of views

1 (1 by UMONS)

Number of downloads

121 (0 by UMONS)

More statistics

Scopus citations^®

Scopus citations^®
without self-citations

OpenCitations

Bibliography

Adomavicius, G., Tuzhilin, A., 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 (2005), 734–749, 10.1109/TKDE.2005.99.
Altingovde, I.S., Subakan, O.N., Ulusoy, O., Cluster searching strategies for collaborative recommendation systems. Information Processing and Management 49:3 (2013), 688–697, 10.1016/j.ipm.2012.07.008.
Balabanović, M., Shoham, Y., Fab: Content-based, collaborative recommendation. Communications of the ACM 40:3 (1997), 66–72, 10.1145/245108.245124.
Banerjee, A., Dhillon, I., Ghosh, J., Merugu, S., Modha, D.S., A generalized maximum entropy approach to Bregman co-clustering and matrix approximation. Journal of Machine Learning Research 8 (2007), 1919–1986.
Breese, J.S., Heckerman, D., Kadie, C., Empirical analysis of predictive algorithms for collaborative filtering. Proceedings of the fourteenth conference on uncertainty in artificial intelligence UAI’98, 1998, Morgan Kaufmann Publishers Inc, San Francisco, CA, USA, 43–52.
Cho, H., Dhillon, I.S., Guan, Y., Sra, S., Minimum sum-squared residue based co-clustering of gene expression data. Proceedings of the fourth SIAM international conference on data mining (SDM), 2004, 114–125.
Cöster, R., Svensson, M., Inverted file search algorithms for collaborative filtering. Proceedings of the twenty fifth annual international ACM SIGIR conference on research and development in information retrieval SIGIR’02, 2002, ACM, New York, NY, USA, 246–252, 10.1145/564376.564420.
DuBois, T., Golbeck, J., Kleint, J., Srinivasan, A., Improving recommendation accuracy by clustering social networks with trust. Proceedings of the fourth ACM RecSys workshop on recommender systems and the social web, 2009, New York, NY USA.
Gantner, Z., Rendle, S., Freudenthaler, C., Schmidt-Thieme, L., Mymedialite: A free recommender system library. Proceedings of the fifth ACM conference on recommender systems RecSys’11, 2011, ACM, New York, NY, USA, 305–308, 10.1145/2043932.2043989.
Gong, S., A collaborative filtering recommendation algorithm based on user clustering and item clustering. Journal of Software 5 (2010), 745–752 Academy Publisher.
Guo, G., Zhang, J., Yorke-Smith, N., Leveraging multiviews of trust and similarity to enhance clustering-based recommender systems. Knowledge-Based Systems 74 (2015), 14–27.
Harper, F.M., Konstan, J.A., The MovieLens datasets: History and context. ACM Transactions on Interactive Intelligent Systems 5:4 (2015), 19:1–19:19, 10.1145/2827872.
He, X., Kan, M.-Y., Xie, P., Chen, X., Comment-based multi-view clustering of web 2.0 items. Proceedings of the twenty third international conference on world wide web WWW ‘14, 2014, ACM, 771–782.
Kohrs, A., Merialdo, B., Clustering for collaborative filtering applications. Proceedings of international conference on computational intelligence for modeling, control and automation, 1999, IOS Press, Vienna, Austria.
Konstan, J.A., Miller, B.N., Maltz, D., Herlocker, J.L., Gordon, L.R., Riedl, J., Grouplens: Applying collaborative filtering to usenet news. Communications of the ACM 40:3 (1997), 77–87, 10.1145/245108.245126.
Koren, Y., Bell, R., Volinsky, C., Matrix factorization techniques for recommender systems. Computer 42:8 (2009), 30–37, 10.1109/MC.2009.263.
Kuzelewska, U., Clustering algorithms in hybrid recommender system on MovieLens data. Studies in Logic, Grammar and Rhetoric 37 (2014), 125–139.
Lee, J., Bengio, S., Kim, S., Lebanon, G., Singer, Y., Local collaborative ranking. Proceedings of the twenty third international conference on world wide web WWW ‘14, 2014, ACM, New York, NY, USA, 85–96, 10.1145/2566486.2567970.
Leskovec, J., Rajaraman, A., Ullman, J.D., Mining of massive datasets. 2nd, 2014, Cambridge University Press, Cambridge.
Linden, G., Smith, B., York, J., Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing 7:1 (2003), 76–80, 10.1109/MIC.2003.1167344.
Ma, X., Lu, H., Gan, Z., Ma, Y., Improving recommendation accuracy with clustering-based social regularization. Web technologies and applications, 2014, Springer International Publishing, 177–188.
Ma, X., Lu, H., Gan, Z., Zhao, Q., An exploration of improving prediction accuracy by constructing a multi-type clustering based recommendation framework. Neurocomputing 191 (2016), 388–397.
McAuley, J., Pandey, R., Leskovec, J., Inferring networks of substitutable and complementary products. Proceedings of the twenty first ACM SIGKDD international conference on knowledge discovery and data mining KDD15, 2015, ACM, New York, NY, USA, 785–794, 10.1145/2783258.2783381.
McAuley, J., Targett, C., Shi, Q., van den Hengel, A., Image-based recommendations on styles and substitutes. Proceedings of the thirty eighth international ACM SIGIR conference on research and development in information retrieval SIGIR’15, 2015, ACM, New York, NY, USA, 43–52, 10.1145/2766462.2767755.
Nathanson, T., Bitton, E., Goldberg, K., Eigentaste 5.0: Constant-time adaptability in a recommender system using item clustering. Proceedings of the 2007 ACM conference on recommender systems RecSys’07, 2007, ACM, New York, NY, USA, 149–152, 10.1145/1297231.1297258.
O'Donovan, J., Smyth, B., Trust in recommender systems. Proceedings of the tenth international conference on intelligent user interfaces IUI ‘05, 2005, ACM, New York, NY, USA, 167–174, 10.1145/1040830.1040870.
Paterek, A., Improving regularized singular value decomposition for collaborative filtering. Proceedings of the thirteenth ACM international conference on knowledge discovery and data mining, KDD CUP workshop at SIGKDD’07, 2007, 39–42 San Jose, California, USA.
Pham, M.C., Cao, Y., Klamma, R., Jarke, M., A clustering approach for collaborative filtering recommendation using social network analysis. Journal of Universal Computer Science 17:4 (2011), 583–604.
Pitsilis, G., Zhang, X., Wang, W., Clustering recommenders in collaborative filtering using explicit trust information. IFIP advances in information and communication technology, 358, 2011.
Quan, T.K., Fuyuki, I., Shinichi, H., Improving accuracy of recommender system by clustering items based on stability of user similarity. Proceedings of the international conference on computational intelligence for modelling control and automation and international conference on intelligent agents web technologies and international commerce CIMCA ‘06, 2006, IEEE Computer Society, Washington, DC, USA, 61–68, 10.1109/CIMCA.2006.123.
Rashid, A.M., Lam, S.K., Karypis, G., Riedl, J., Clustknn: A highly scalable hybrid model- and memory-based CF algorithm. Proceedings of the WebKDD-06, KDD workshop on web mining and web usage analysis, at twelfth ACM SIGKDD international conference on knowledge discovery and data mining, 2006.
Sarwar, B., Karypis, G., Konstan, J., Riedl, J., Item-based collaborative filtering recommendation algorithms. Proceedings of the tenth international conference on world wide web WWW ‘01, 2001, ACM, New York, NY, USA, 285–295, 10.1145/371920.372071.
Sarwar, B., Karypis, G., Konstan, J.A., Riedl, J.T., Application of dimensionality reduction in recommender system – A case study. Proceedings of the ACM WebKDD 2000 web mining for e-commerce workshop, 2000.
Sarwar, B.M., Karypis, G., Konstan, J., Riedl, J., Recommender systems for large-scale e-commerce: Scalable neighborhood formation using clustering. Proceedings of the fifth international conference on computer and information technology, 2002.
Selamat, A., Moghaddam, S.G., Improved collaborative filtering on recommender based systems using smoothing density-based user clustering. International Journal of Advance Computer Technology 4 (2012), 352–359 ACM.
Wei, S., Ye, N., Zhang, S., Huang, X., Collaborative filtering recommendation algorithm based on item clustering and global similarity. Proceedings of the 2012 fifth international conference on business intelligence and financial engineering (BIFE), 2012, IEEE, Lanzhou.
Xue, G.-R., Lin, C., Yang, Q., Xi, W., Zeng, H.-J., Yu, Y., et al. Scalable collaborative filtering using cluster-based smoothing. Proceedings of the twenty eighth annual international ACM SIGIR conference on research and development in information retrieval SIGIR ‘05, 2005, ACM, New York, NY, USA, 114–121, 10.1145/1076034.1076056.
Zhang, W., Wu, B., Liu, Y., Cluster-level trust prediction based on multi-modal social networks. Neurocomputing 210 (2016), 206–216.