A feature-based regression algorithm for cold-start recommendation

References

• Bell, R., Y. Koren and C. Volinsky, “Modeling relationships at multiple scales to improve accuracy of large recommenders,” Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, Aug. 12–15, CA, USA, 95–104 (2007).
   Google Scholar
• Bobadilla, J., F. Ortega, A. Hernando and J. Bernal, “A collaborative filtering approach to mitigate the new user cold start problem,” Knowledge-Based Systems, 26, 225–238 (2012).
   Web of Science ®Google Scholar
• Gantner, Z., L. Drumond, C. Freudenthaler, S. Rendle and L. Schmidt-Thieme, “Learning attribute-to-feature mappings for cold-start recommendations,” Proceedings of the 2010 IEEE International Conference on Data Mining, Dec. 14–17, Sydney, Australia, 176–185(2010).
   Google Scholar
• Goldberg, D., D. Nichols, B. M. Oki and D. Terry, “Using collaborative filtering to weave an information tapestry,” Communications of the ACM, 35, 61–70 (1992).
   Web of Science ®Google Scholar
• Good, N., J. B. Schafer, J. A. Konstan, A. Borchers, B. Sarwar, J. Herlocker, and J. Riedl. “Combining collaborative filtering with personal agents for better recommendations,” Proceeding AAAI ‘99/IAAI ‘99 Proceedings of the 16th national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence, Jul. 18–22, CA, USA, 439–446 (1999).
   Google Scholar
• Herlocker, J. L., J. A. Konstan, A. Borchers and J. Riedl, “An algorithmic framework for performing collaborative filtering,” Proceedings of the 1999 Conference on Research and Development in Information Retrieval, Aug. 15–19, CA, USA, 230–237 (1999).
   Google Scholar
• Koren, Y., “Factorization meets the neighborhood: A multifaceted collaborative filtering model,” Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, Aug. 24–27, New York, NY, 426–434 (2008).
   Google Scholar
• Koren, Y., “Factor in the neighbors: Scalable and accurate collaborative filtering,” ACM Transactions on Knowledge Discovery from Data, 4, 1–11 (2010).
   Web of Science ®Google Scholar
• Lauw, H. W., E. P. Lim and K. Wang, “Bias and controversy in evaluation systems,” IEEE Transactions on Knowledge and Data Engineering, 20, 1490–1504 (2008).
   Web of Science ®Google Scholar
• Meyffret, S., L. Médini and F. Laforest, “Trust-based local and social recommendation,” Proceeding RSWeb ‘12 Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web, Sep. 9–13, Bublin, Ireland, 53–60 (2012).
   Google Scholar
• Mohsen, J. and M. Ester, “TrustWalker: A random walk model for combining trust-based and item-based recommendation,” Proceedings of the 15th SIGKDD international conference on Knowledge discovery and data mining, Jul. 28–Jul. 1, Paris, France, 397–406 (2009).
   Google Scholar
• Park, S. T., D. Pennock, O. Madani, N. Good and D. DeCoste, “Naive filterbots for robust cold-start recommendations,” Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, Aug. 20–23, Philadelphia, USA, 699–705 (2006).
   Google Scholar
• Park, S. T. and W. Chu, “Pairwise preference regression for cold-start recommendation,” Proceedings of the third ACM conference on Recommender systems, Oct. 22–25, CA, USA, 21–28 (2009).
   Google Scholar
• Pazzani, M. J., “A framework for collaborative, content-based and demographic filtering,” Artificial Intelligence Review, 13, 393–408 (1999).
   Google Scholar
• Polat, H. and W. L. Du, “SVD-based collaborative filtering with privacy,” Proceedings of the 2005 ACM Symposium on Applied Computing, Mar. 13–17, Santa Fe, NM, 791–795 (2005).
   Google Scholar
• Salakhutdinov, R., A. Mnih and G. Hinton, “Restricted Boltzmann machines for collaborative filtering,” Proceedings of the 24-th International Conference on Machine Learning, OR, USA, 791–798 (2007).
   Google Scholar
• Sarwar, B., G. Karypis, J. Konstan and J. Riedl, “Item-based collaborative filtering recommendation algorithms,” Proceedings of the 10th international conference on World Wide Web, May 1–5, Hong Kong, China, 285–295 (2001).
   Google Scholar
• Seung, D. and L. Lee, “Algorithms for non-negative matrix factorization,” Advances in neural information processing systems, 13, 556–562 (2001).
   Google Scholar
• Vozalis, M. and K. Margaritis, “Using SVD and demographic data for the enhancement of generalized collaborative filtering,” Information Sciences, 177, 3017–3037 (2007).
   Web of Science ®Google Scholar
• Zhou, K., S. H. Yang and H. Y. Zha, “Functional matrix factorizations for cold-start recommendation,” Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, Jul. 24–27, Beijing, China, 315–324 (2011).
   Google Scholar