| Reference: | [1] How retailers can keep up with consumers. Retrieved October 2013. from: http://www.mckinsey.com/industries/retail/our-insights/how-retailers-can-keep-up-with-consumers
[2] Netflix Prize. from: http://www.netflixprize.com/community/forum.html
[3] YouTube statistics. from: https://www.youtube.com/yt/press/statistics.html
[4] Davidson, J., Liebald, B., Liu, J., Nandy, P., Van Vleet, T., Gargi, U., Gupta, S., He, Y., Lambert, M., Livingston, B., & Sampath, D. (2010). The YouTube video recommendation system. In Proceedings of the fourth ACM conference on Recommender systems. 293–296.
[5] Facebook newsroom, company info, statistics. from: https://www.youtube.com/yt/press/zh-TW/statistics
[6] Recommending items to more than a billion people. Retrieved June 3 2015. from: https://code.facebook.com/posts/861999383875667/recommending-items-to-morm-than-a-billion-people/
[7] Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992). Using collaborative filtering to weave an information tapestry. Communications of the ACM. 35(12). 61-70.
[8] Konstan, J. A., Miller, B. N., Maltz, D., Herlocker, J. L., Gordon, L. R., & Riedl, J. (1997). GroupLens: applying collaborative filtering to Usenet news. Communications of the ACM. 40(3). 77-87.
[9] Harper, F. M., & Konstan, J. A. (2016). The MovieLens datasets: history and context. ACM Transactions on Interactive Intelligent Systems (TiiS). 5(4). Article No.: 19. 1-20.
[10] Linden, G., Smith, B., & York, J. (2003). Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet computing. 7(1). 76-80.
[11] Covington, P., Adams, J., & Sargin, E. (2016). Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems. 191-198.
[12] Bell, R. M., & Koren, Y. (2007). Lessons from the Netflix prize challenge. ACM Sigkdd Explorations Newsletter. 9(2). 75-79.
[13] Listen to Pandora, and it listens back. Retrieved January 4 2014. from: https://www.nytimes.com/2014/01/05/technology/pandora-mines-users-data-to-better-target-ads.html?_r=0
[14] Ali, K., & Van Stam, W. (2004). TiVo: making show recommendations using a distributed collaborative filtering architecture. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. 394-401.
[15] Pazzani, M. J. (1999). A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review. 13(5-6). 393-408.
[16] Pazzani, M. J., & Billsus, D. (2007). Content-based recommendation systems. The adaptive web. 325-341.
[17] Ramos, J. (2003). Using tf-idf to determine word relevance in document queries. In Proceedings of the first instructional conference on machine learning.
[18] Tata, S., & Patel, J. M. (2007). Estimating the selectivity of TF-IDF based cosine similarity predicates. ACM Sigmod Record. 36(2). 7-12.
[19] Huang, A. (2008). Similarity measures for text document clustering. In Proceedings of the sixth New Zealand computer science research student conference. 49-56.
[20] Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS). 22(1). 5-53.
[21] Papagelis, M., & Plexousakis, D. (2005). Qualitative analysis of user-based and item-based prediction algorithms for recommendation agents. Engineering Applications of Artificial Intelligence. 18(7). 781-789.
[22] Adomavicius, G., & 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(6). 734-749.
[23] Wang, J., De Vries, A. P., & Reinders, M. J. (2006). Unifying user-based 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. 501-508.
[24] Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web. 285-295.
[25] Yildirim, H., & Krishnamoorthy, M. S. (2008). A random walk method for alleviating the sparsity problem in collaborative filtering. In Proceedings of the 2008 ACM conference on Recommender systems. 131-138.
[26] Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer. 42(8). 30-37.
[27] Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2000). Application of dimensionality reduction in recommender system-a case study. Minnesota Univ Minneapolis Dept of Computer Science.
[28] Paterek, A. (2007). Improving regularized singular value decomposition for collaborative filtering. In Proceedings of KDD cup and workshop. Vol. 2007. 5-8.
[29] Netflix Update: Try This at Home. Retrieved December 2006. from: http://sifter.org/~simon/journal/20061211.html
[30] Rendle, S., Freudenthaler, C., Gantner, Z., & Schmidt-Thieme, L. (2009). BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence. 452-461.
[31] Bennett, J., & Lanning, S. (2007). The Netflix Prize. In Proceedings of KDD cup and workshop. Vol. 2007. 3-6.
[32] Hu, Y., Koren, Y., & Volinsky, C. (2008). Collaborative filtering for implicit feedback datasets. In Data Mining, 2008. ICDM`08. Eighth IEEE International Conference. 263-272.
[33] Surprise (Python scikit for recommender systems), Similarities Module Introduction. from: http://surprise.readthedocs.io/en/latest/similarities.html
[34] Koren, Y. (2010). Factor in the neighbors: scalable and accurate collaborative filtering. ACM Transactions on Knowledge Discovery from Data. 4(1). Article No.: 1. 1-24.
[35] Surprise (Python scikit for recommender systems), Matrix Factorization-based algorithms. from: http://surprise.readthedocs.io/en/latest/matrix_factorization.html
[36] Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2000). Analysis of recommendation algorithms for e-commerce. In Proceedings of the 2nd ACM conference on Electronic commerce. 158-167.
[37] What do Recommender Systems experts think of the "Estimating the causal impact of recommendation systems from observational data" paper ? Answer by Xavier Amatriain. from: https://www.quora.com/What-do-Recommender-
Systems-experts-think-of-the-Estimating-the-causal-impact-of-recommendation-systems-from-observational-data-paper
[38] Baltrunas, L., & Amatriain, X. (2009). Towards time-dependant recommendation based on implicit feedback. In Workshop on context-aware recommender systems (CARS’09).
[39] Gomez-Uribe, C. A., & Hunt, N. (2016). The netflix recommender system: Algorithms, business value, and innovation. ACM Transactions on Management Information Systems (TMIS). 6(4). Article No.: 13. 1-19. |
