A Logistic Factorization Model for Recommender Systems With Multinomial Responses

References

• Aciar, S., Zhang, D., Simoff, S., and Debenham, J. (2006), “Recommender System Based on Consumer Product Reviews,” in Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence, WI ’06, Washington, DC, USA, IEEE Computer Society, pp. 719–723.
   Google Scholar
• Adomavicius, G., and 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, 734–749. DOI: 10.1109/TKDE.2005.99.
   Web of Science ®Google Scholar
• Agarwal, D., and Chen, B.-C. (2009), “Regression-Based Latent Factor Models,” in Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’09, New York, NY, USA, ACM, pp. 19–28.
   Google Scholar
• Aggarwal, C. C. (2016), Recommender Systems, Cham: Springer.
   Google Scholar
• Agresti, A., and Kateri, M. (2011), “Categorical Data Analysis,” in International Encyclopedia of Statistical Science, ed. M. Lovric, Berlin, Heidelberg: Springer, pp. 206–208, DOI: 10.1007/978-3-642-04898-2_161.
   Google Scholar
• Bell, R. M., and Koren, Y. (2007), “Lessons From the Netflix Prize Challenge,” SIGKDD Explorations Newsletter, 9, 75–79. DOI: 10.1145/1345448.1345465.
   Google Scholar
• Bi, X., Qu, A., Wang, J., and Shen, X. (2017), “A Group-Specific Recommender System,” Journal of the American Statistical Association, 112, 1344–1353. DOI: 10.1080/01621459.2016.1219261.
   Web of Science ®Google Scholar
• Blanco-Fernandez, Y., Pazos-Arias, J. J., Gil-Solla, A., Ramos-Cabrer, M., and Lopez-Nores, M. (2008), “Providing Entertainment by Content-Based Filtering and Semantic Reasoning in Intelligent Recommender Systems,” IEEE Transactions on Consumer Electronics, 54, 727–735. DOI: 10.1109/TCE.2008.4560154.
   Web of Science ®Google Scholar
• Bobadilla, J., Ortega, F., Hernando, A., and Alcalá, J. (2011), “Improving Collaborative Filtering Recommender System Results and Performance Using Genetic Algorithms,” Knowledge-Based Systems, 24, 1310–1316. DOI: 10.1016/j.knosys.2011.06.005.
   Web of Science ®Google Scholar
• Cacheda, F., Carneiro, V., Fernández, D., and Formoso, V. (2011), “Comparison of Collaborative Filtering Algorithms: Limitations of Current Techniques and Proposals for Scalable, High-Performance Recommender Systems,” ACM Transactions on the Web, 5, 2:1–2:33. DOI: 10.1145/1921591.1921593.
   Web of Science ®Google Scholar
• Cron, A., Zhang, L., and Agarwal, D. (2014), “Collaborative Filtering for Massive Multinomial Data,” Journal of Applied Statistics, 41, 701–715. DOI: 10.1080/02664763.2013.847072.
   Web of Science ®Google Scholar
• Davidson, J., Liebald, B., Liu, J., Nandy, P., Van Vleet, T., Gargi, U., Gupta, S., He, Y., Lambert, M., Livingston, B., and Sampath, D. (2010), “The YouTube Video Recommendation System,” in Proceedings of the Fourth ACM Conference on Recommender Systems, RecSys ’10, New York, NY, USA, pp. 293–296.
   Google Scholar
• Ekstrand, M. D., Riedl, J. T., and Konstan, J. A. (2011), “Collaborative Filtering Recommender Systems,” Foundations and Trends in Human-Computer Interaction, 4, 81–173. DOI: 10.1561/1100000009.
   Google Scholar
• Ghosh, S., Mundhe, M., Hernandez, K., and Sen, S. (1999), “Voting for Movies: The Anatomy of a Recommender System,” in Proceedings of the Third Annual Conference on Autonomous Agents, AGENTS ’99, New York, NY, pp. 434–435.
   Google Scholar
• Goldberg, K., Roeder, T., Gupta, D., and Perkins, C. (2000), “Eigentaste: A Constant Time Collaborative Filtering Algorithm,” Information Retrieval, 4, 133–151. DOI: 10.1023/A:1011419012209.
   Google Scholar
• Gomez-Uribe, C. A., and Hunt, N. (2015), “The Netflix Recommender System: Algorithms, Business Value, and Innovation,” ACM Transactions on Management Information Systems, 6, 13:1–13:19. DOI: 10.1145/2843948.
   Web of Science ®Google Scholar
• Gunawardana, A., and Shani, G. (2015), “Evaluating Recommender Systems,” in Recommender Systems Handbook, eds. F. Ricci, L. Rokach, and B. Shapira, Boston, MA: Springer, pp. 265–308.
   Google Scholar
• Harper, F. M., and Konstan, J. A. (2015), “The MovieLens Datasets: History and Context,” ACM Transactions on Interactive Intelligent Systems, 5, 19:1–19:19. DOI: 10.1145/2827872.
   Web of Science ®Google Scholar
• Iaquinta, L., De Gemmis, M., Lops, P., Semeraro, G., Filannino, M., and Molino, P. (2008), “Introducing Serendipity in a Content-Based Recommender System,” in Eighth International Conference on Hybrid Intelligent Systems, 2008. HIS’08, IEEE, pp. 168–173.
   Google Scholar
• Kim, Y. S., Yum, B.-J., Song, J., and Kim, S. M. (2005), “Development of a Recommender System Based on Navigational and Behavioral Patterns of Customers in e-Commerce Sites,” Expert Systems With Applications, 28, 381–393. DOI: 10.1016/j.eswa.2004.10.017.
   Web of Science ®Google Scholar
• Koenigstein, N., Dror, G., and Koren, Y. (2011), “Yahoo! Music Recommendations: Modeling Music Ratings With Temporal Dynamics and Item Taxonomy,” in Proceedings of the Fifth ACM Conference on Recommender Systems, RecSys ’11, New York, NY, USA, pp. 165–172.
   Google Scholar
• Koren, Y. (2010), “Factor in the Neighbors: Scalable and Accurate Collaborative Filtering,” ACM Transactions on Knowledge Discovery From Data, 4, 1:1–1:24. DOI: 10.1145/1644873.1644874.
   Web of Science ®Google Scholar
• Koren, Y., Bell, R., and Volinsky, C. (2009), “Matrix Factorization Techniques for Recommender Systems,” Computer, 42, 30–37. DOI: 10.1109/MC.2009.263.
   Web of Science ®Google Scholar
• Levi, A., Mokryn, O., Diot, C., and Taft, N. (2012), “Finding a Needle in a Haystack of Reviews: Cold Start Context-Based Hotel Recommender System,” in Proceedings of the Sixth ACM Conference on Recommender Systems, RecSys ’12, New York, NY, pp. 115–122.
   Google Scholar
• Lops, P., De Gemmis, M., and Semeraro, G. (2011), “Content-Based Recommender Systems: State of the Art and Trends,” in Recommender Systems Handbook, eds. F. Ricci, L. Rokach, B. Shapira, and P. Kantor, Boston, MA: Springer, pp. 73–105.
   Google Scholar
• Lu, J., Wu, D., Mao, M., Wang, W., and Zhang, G. (2015), “Recommender System Application Developments: A Survey,” Decision Support Systems, 74, 12–32. DOI: 10.1016/j.dss.2015.03.008.
   Web of Science ®Google Scholar
• Mazumder, R., Hastie, T., and Tibshirani, R. (2010), “Spectral Regularization Algorithms for Learning Large Incomplete Matrices,” Journal of Machine Learning Research, 11, 2287–2322.
   PubMed Web of Science ®Google Scholar
• Melville, P., Mooney, R. J., and Nagarajan, R. (2002), “Content-Boosted Collaborative Filtering for Improved Recommendations,” in Eighteenth National Conference on Artificial Intelligence, Menlo Park, CA, USA, American Association for Artificial Intelligence, pp. 187–192.
   Google Scholar
• Miller, B. N., Albert, I., Lam, S. K., Konstan, J. A., and Riedl, J. (2003), “MovieLens Unplugged: Experiences With an Occasionally Connected Recommender System,” in Proceedings of the 8th International Conference on Intelligent User Interfaces, IUI ’03, New York, NY, ACM, pp. 263–266.
   Google Scholar
• Mirbakhsh, N., and Ling, C. X. (2015), “Improving Top-N Recommendation for Cold-Start Users via Cross-Domain Information,” ACM Transactions on Knowledge Discovery From Data, 9, 33:1–33:19. DOI: 10.1145/2724720.
   Web of Science ®Google Scholar
• Mooney, R. J., and Roy, L. (2000), “Content-Based Book Recommending Using Learning for Text Categorization,” in Proceedings of the Fifth ACM Conference on Digital Libraries, DL ’00, New York, NY, pp. 195–204.
   Google Scholar
• Nguyen, A.-T., Denos, N., and Berrut, C. (2007), “Improving New User Recommendations With Rule-Based Induction on Cold User Data,” in Proceedings of the 2007 ACM Conference on Recommender Systems, RecSys ’07, New York, NY, pp. 121–128.
   Google Scholar
• Oghina, A., Breuss, M., Tsagkias, M., and De Rijke, M. (2012), “Predicting IMDB Movie Ratings Using Social Media,” in Advances in Information Retrieval, Lecture Notes in Computer Science, Berlin, Heidelberg: Springer, pp. 503–507.
   Google Scholar
• Park, S.-T., Pennock, D., Madani, O., Good, N., and DeCoste, D. (2006), “NaiVe Filterbots for Robust Cold-Start Recommendations,” in Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’06, New York, NY, USA, pp. 699–705.
   Google Scholar
• Resnick, P., and Varian, H. R. (1997), “Recommender Systems,” Communications of the ACM, 40, 56–58. DOI: 10.1145/245108.245121.
   Web of Science ®Google Scholar
• Ricci, F., Rokach, L., and Shapira, B. (2011), “Introduction to Recommender Systems Handbook,” in Recommender Systems Handbook, Boston, MA: Springer, pp. 1–35.
   Google Scholar
• Salakhutdinov, R., Mnih, A., and Hinton, G. (2007), “Restricted Boltzmann Machines for Collaborative Filtering,” in Proceedings of the 24th International Conference on Machine Learning, ICML ’07, New York, NY, USA, pp. 791–798.
   Google Scholar
• Spall, J. C. (2005), Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control (Vol. 65), Hoboken, NJ: Wiley.
   Google Scholar
• Srebro, N., Alon, N., and Jaakkola, T. S. (2005), “Generalization Error Bounds for Collaborative Prediction With Low-Rank Matrices,” in Advances in Neural Information Processing Systems (Vol. 17), eds. L. K. Saul, Y. Weiss, and L. Bottou, Cambridge, MA: MIT Press, pp. 1321–1328.
   Google Scholar
• Stern, D., Herbrich, R., and Graepel, T. (2009), “Matchbox: Large Scale Bayesian Recommendations,” in Proceedings of the 18th International World Wide Web Conference.
   Google Scholar
• Zhu, Y., Shen, X., and Ye, C. (2016), “Personalized Prediction and Sparsity Pursuit in Latent Factor Models,” Journal of the American Statistical Association, 111, 241–252. DOI: 10.1080/01621459.2014.999158.
   Web of Science ®Google Scholar