Abstract
Collaborative Filtering (CF) assists Recommender Systems (RSs) in recommending products or services that they are likely to be of interest to users. Various CF schemes have been proposed, but most of them are seriously limited by a cold-start problem which refers to a situation that RSs are incapable of drawing recommendations for new items, new users or both. Moreover,insignificant ratings whose values are less than the corresponding average ratings adversely affect recommendations.In this paper, we propose a Credible and cO-clustering filterBot for cold-stArt recommendations (COBA). It filtersinsignificant ratings by introducing rating confidence level, which substantially reduces the dimensionality of the item-user matrix. To overcome data sparsity, COBA co-clusters items and users, and smoothes ratings within every user cluster. Finally, it predicts user preference byfusing recommendations from item and user clusters. Our experiments show that COBA solves the cold-start problem regarding recommendation accuracy and scalability.
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References
Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Computing 7(1), 76–80 (2003)
Das, A.S., Datar, M., Garg, A., Rajaram, S.: Google news personalization: scalable online collaborative filtering. In: Proc. of the 16th Intl. Conf. on World Wide Web, pp. 271–280 (2007)
Bennett, J., Lanning, S.: Netflix: The Netflix prize. In: KDD Cup and Workshop in Conjunction with ACM SIGKDD, pp. 201–206 (2007)
Marlin, B., Zemel, R.S., Roweis, S., Slaney, M.: Collaborative filtering and the missing at random assumption. In: Proc. of the 23rd Conf. on Uncertainty in Artificial Intelligence (2007)
Xue, G.R., Lin, C., Yang, Q., et al.: Scalable collaborative filtering using clusterbased smoothing. In: Proc. of the 28th Intl. Conf. on Research and Development in Information Retrieval, pp. 114–121 (2005)
Wang, J., de Vries, A.P., Reinders, M.J.T.: Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In: Proc. of the 29th Intl. Conf. on Research and Development in Information Retrieval, pp. 501–508 (2006)
Daqiang, Z., Jiannong, C., Minyi, G., Jingyu, Z., Vaskar, R.: An efficient collaborative filtering approach using smoothing and fusing. In: Proc. of the 38th Intl. Conf. on Parallel Processing, pp. 147–154 (2009)
Bell, R., Koren, Y., Volinsky, C.: Modeling relationships at multiple scales to improve accuracy of large recommender systems. In: Proc. of the 13th Intl. Conf. on Knowledge Discovery and Data Mining, pp. 95–104 (2007)
Victor, P., Cornelis, C., Teredesai, A.M., De Cock, M.: Whom should I trust?: the impact of key figures on cold start recommendations. In: Proc. of the ACM Symposium on Applied Computing, pp. 2014–2018 (2008)
Schein, A., Popescul, A., Ungar, L., Pennock, D.: Methods and metrics for coldstart recommendations. In: Proc. of the 25th Intl. Conf. on Research and Development in Information Retrieval, pp. 253–260 (2002)
Bell, R.M., Koren, Y.: Scalable collaborative filtering with jointly derived neighborhood interpolation weights. In: Intl. Conf. on Data Mining, pp. 43–52 (2007)
Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proc. of the 14th Intl. Conf. on Knowledge Discovery and Data Mining, pp. 426–434 (2008)
Park, S., Pennock, D., Madani, O., Good, N., DeCoste, D.: Naïve filterbots for robust cold-start recommendations. In: Proc. of the 12th Intl. Conf. on Knowledge Discovery and Data Mining, pp. 705–712 (2006)
Ahn, H.J.: A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem. Information Sciences 178(1), 37–51 (2008)
Schein, A., Popescul, A., Ungar, L., Pennock, D.: Generative models for coldstart recommendations. In: Proc. of the 2001 SIGIR Workshop on Recommender Systems, vol. 6, pp. 10–15 (2001)
Zhang, W.: Relational distance-based collaborative filtering. In: Proc. of the 31st Intl. Conf. on Research and Development in Information Retrieval, pp. 877–878 (2008)
Chu, W., Park, S.T.: Personalized recommendation on dynamic content using predictive bilinear models. In: Proc. of the 18th Intl. Conf. on World Wide Web, pp. 691–700 (2009)
Yu, K., Schwaighofer, A., Tresp, V., Ma, W., Zhang, H.: Collaborative ensemble learning: Combining collaborative and content-based information filtering via hierarchical bayes. In: Proc. of the 19th Conf. on Uncertainty in Artificial Intelligence, pp. 616–623 (2003)
Basilico, J., Hofmann, T.: Unifying collaborative and content-based filtering. In: Proc. of the 21st Intl. Conf. on Machine Learning, p. 9 (2004)
Good, N., Schafer, J.B., Konstan, J.A., Borchers, A., et al.: Combining collaborative filtering with personal agents for better recommendations. In: Proc. of the 16th AAAI and the 11th IAAI, pp. 439–446 (1999)
Dhillon, I.S., Mallela, S., Modha, D.S.: Information-theoretic co-clustering. In: Proc. of the 9th Intl. Conf. on Knowledge Discovery and Data Mining, pp. 89–98 (2003)
George, T., Merugu, S.: A scalable collaborative filtering framework based on co-clustering. In: Proc. of the 5th Intl. Conf. on Data Mining, pp. 625–628 (2005)
Deodhar, M., Ghosh, J.: A framework for simultaneous co-clustering and learning from complex data. In: Proc. of the 13th Intl. Conf. on Knowledge Discovery and Data Mining, pp. 250–259 (2007)
Chen, G., Wang, F., Zhang, C.: Collaborative filtering using orthogonal nonnegative matrix tri-factorization. Information Processing and Management 45(3), 368–379 (2009)
Luo, H., Niu, C., Shen, R., Ullrich, C.: A collaborative filtering framework based on both local user similarity and global user similarity. Machine Learning 72(3), 231–245 (2008)
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Wang, W., Zhang, D., Zhou, J. (2011). COBA: A Credible and Co-clustering Filterbot for Cold-Start Recommendations. In: Wang, Y., Li, T. (eds) Practical Applications of Intelligent Systems. Advances in Intelligent and Soft Computing, vol 124. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25658-5_56
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DOI: https://doi.org/10.1007/978-3-642-25658-5_56
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