Abstract
With the exponential increase of Web communities, community recommendation has become increasingly important in sifting valuable and interesting communities for users. In this paper, we study the problem of novel community recommendation, and propose a method based on a user-community total relation (i.e. user-user, community-community, and user-community interactions). Our novel recommendation method suggests communities that the target user has not seen but is potentially interested in, in order to broaden the user’s horizon. Specifically, a Weighted Latent Dirichlet Allocation (WLDA) algorithm improves recommendation accuracy utilizing social relations. A definition of community novelty together with an algorithm for novelty computation are further proposed based on the total relation. Finally, a multi-objective optimization strategy improves the overall recommendation quality by combining accuracy and novelty scores. Experimental results on a real dataset show that our proposed method outperforms state-of-the-art recommendation methods on both accuracy and novelty.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS) 22(1), 5–53 (2004)
Vargas, S., Castells, P.: Rank and relevance in novelty and diversity metrics for recommender systems. In: Proceedings of the Fifth ACM Conference on Recommender Systems, pp. 109–116. ACM (2011)
Ma, H., Yang, H., Lyu, M.R., King, I.: Sorec: Social recommendation using probabilistic matrix factorization. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, pp. 931–940. ACM (2008)
Jamali, M., Ester, M.: Trustwalker: A random walk model for combining trust-based and item-based recommendation. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 397–406. ACM (2009)
Chen, W.-Y., Chu, J.-C., Luan, J., Bai, H., Wang, Y., Chang, E.Y.: Collaborative filtering for orkut communities: Discovery of user latent behavior. In: Proceedings of the 18th International Conference on World Wide Web, pp. 681–690. ACM (2009)
Deshpande, M., Karypis, G.: Item-based top-n recommendation algorithms. ACM Transactions on Information Systems (TOIS) 22(1), 143–177 (2004)
Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 230–237. ACM (1999)
Onuma, K., Tong, H., Faloutsos, C.: Tangent: A novel, surprise me, recommendation algorithm. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 657–666. ACM (2009)
McNee, S.M., Riedl, J., Konstan, J.A.: Being accurate is not enough: how accuracy metrics have hurt recommender systems. In: CHI 2006 Extended Abstracts on Human Factors in Computing Systems, pp. 1097–1101. ACM (2006)
Oh, J., Park, S., Yu, H., Song, M., Park, S.-T.: Novel recommendation based on personal popularity tendency. In: 2011 IEEE 11th International Conference on Data Mining (ICDM), pp. 507–516. IEEE (2011)
Merton, R.K.: The matthew effect in science. Science 159(3810), 56–63 (1968)
Anderson, C.: The long tail: Why the future of business is selling less of more. Hyperion Books (2008)
Nakatsuji, M., Fujiwara, Y., Tanaka, A., Uchiyama, T., Fujimura, K., Ishida, T.: Classical music for rock fans?: Novel recommendations for expanding user interests. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 949–958. ACM (2010)
McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: Homophily in social networks. Annual Review of Sociology, 415–444 (2001)
Kamahara, J., Asakawa, T., Shimojo, S., Miyahara, H.: A community-based recommendation system to reveal unexpected interests. In: Proceedings of the 11th International Multimedia Modelling Conference, MMM 2005, pp. 433–438. IEEE (2005)
Kawamae, N.: Serendipitous recommendations via innovators. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 218–225. ACM (2010)
Zhang, Y.C., Saghdha, D., Quercia, D., Jambor, T.: Auralist: Introducing serendipity into music recommendation. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, pp. 13–22. ACM (2012)
Wang, J., De Vries, A.P., Reinders, M.J.: Unifying userbased and item-based collaborative filtering approaches by similarity fusion. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 501–508. ACM (2006)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. The Journal of Machine Learning Research 3, 993–1022 (2003)
Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: ACM SIGMOD Record, vol. 22, pp. 207–216. ACM (1993)
Chen, W.-Y., Zhang, D., Chang, E.Y.: Combinational collaborative filtering for personalized community recommendation. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 115–123. ACM (2008)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
Fouss, F., Pirotte, A., Renders, J.-M., Saerens, M.: Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Transactions on Knowledge and Data Engineering 19(3), 355–369 (2007)
Ma, H.: An experimental study on implicit social recommendation. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 73–82. ACM (2013)
Singla, P., Richardson, M.: Yes, there is a correlation:-from social networks to personal behavior on the web. In: Proceedings of the 17th International Conference on World Wide Web, pp. 655–664. ACM (2008)
Ziegler, C.-N., Lausen, G., Schmidt-Thieme, L.: Taxonomy-driven computation of product recommendations. In: Proceedings of the Thirteenth ACM International Conference on Information and Knowledge Management, pp. 406–415. ACM (2004)
Huang, J., Sun, H., Han, J., Deng, H., Sun, Y., Liu, Y.: Shrink: A structural clustering algorithm for detecting hierarchical communities in networks. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 219–228. ACM (2010)
Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proceedings of the National academy of Sciences of the United States of America 101(suppl. 1), 5228–5235 (2004)
Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of ir techniques. ACM Transactions on Information Systems (TOIS) 20(4), 422–446 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Yu, Q., Peng, Z., Hong, L., Liu, B., Peng, H. (2014). Novel Community Recommendation Based on a User-Community Total Relation. In: Bhowmick, S.S., Dyreson, C.E., Jensen, C.S., Lee, M.L., Muliantara, A., Thalheim, B. (eds) Database Systems for Advanced Applications. DASFAA 2014. Lecture Notes in Computer Science, vol 8422. Springer, Cham. https://doi.org/10.1007/978-3-319-05813-9_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-05813-9_19
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05812-2
Online ISBN: 978-3-319-05813-9
eBook Packages: Computer ScienceComputer Science (R0)