ABSTRACT
Since the Netflix $1 million Prize, announced in 2006, our company has been known to have personalization at the core of our product. Even at that point in time, the dataset that we released was considered "large", and we stirred innovation in the (Big) Data Mining research field. Our current product offering is now focused around instant video streaming, and our data is now many orders of magnitude larger. Not only do we have many more users in many more countries, but we also receive many more streams of data. Besides the ratings, we now also use information such as what our members play, browse, or search.
In this paper, we will discuss the different approaches we follow to deal with these large streams of data in order to extract information for personalizing our service. We will describe some of the machine learning models used, as well as the architectures that allow us to combine complex offline batch processes with real-time data streams.
- X. Amatriain. Mining large streams of user data for personalized recommendations. ACM SIGKDD Explorations Newsletter, 14(2):37--48, 2013. Google ScholarDigital Library
- X. Amatriain and J. Basilico. System architectures for personalization and recommendation. In the Netflix Techblog: http://techblog.netflix.com/2013/03/system-architectures-for.html, March 2013.Google Scholar
- X. Amatriain, J. M. Pujol, and N. Oliver. I Like It... I Like It Not: Evaluating User Ratings Noise in Recommender Systems. In User Modeling, Adaptation, and Personalization, volume 5535, chapter 24, pages 247--258. Springer Berlin, 2009. Google ScholarDigital Library
- R. M. Bell and Y. Koren. Lessons from the Netflix Prize Challenge. SIGKDD Explor. Newsl., 9(2):75--79, December 2007. Google ScholarDigital Library
- O. Chapelle and S. S. Keerthi. Efficient algorithms for ranking with SVMs. Information Retrieval, 13:201--215, June 2010. Google ScholarDigital Library
- Y. Freund, R. Iyer, R. E. Schapire, and Y. Singer. An efficient boosting algorithm for combining preferences. J. Mach. Learn. Res., 4:933--969, December 2003. Google ScholarDigital Library
- S. Funk. Netflix update: Try this at home. http://sifter.org/simon/journal/20061211.html, 2006.Google Scholar
- J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl. Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst., 22(1):5--53, 2004. Google ScholarDigital Library
- M. Karimzadehgan, W. Li, R. Zhang, and J. Mao. A stochastic learning-to-rank algorithm and its application to contextual advertising. In Proceedings of the 20th WWW, 2011. Google ScholarDigital Library
- Y. Koren. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD, 2008. Google ScholarDigital Library
- Y. Koren. Collaborative filtering with temporal dynamics. In Proceedings of the 15th ACM SIGKDD, 2009. Google ScholarDigital Library
- Y. Koren, R. Bell, and C. Volinsky. Matrix Factorization Techniques for Recommender Systems. Computer, 42(8):30--37, August 2009. Google ScholarDigital Library
- N. Lathia, S. Hailes, L. Capra, and X. Amatriain. Temporal diversity in recommender systems. In SIGIR '10: Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval, pages 210--217, New York, NY, USA, 2010. ACM. Google ScholarDigital Library
- E. Pampalk, T. Pohle, and G. Widmer. Dynamic playlist generation based on skipping behavior. In ISMIR, volume 5, pages 634--637, 2005.Google Scholar
- K. Raman, P. Shivaswamy, and T. Joachims. Online learning to diversify from implicit feedback. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '12, pages 705--713, New York, NY, USA, 2012. ACM. Google ScholarDigital Library
- R. Salakhutdinov, A. Mnih, and G. E. Hinton. Restricted Boltzmann machines for collaborative filtering. In Proc of ICML '07, 2007. Google ScholarDigital Library
- Y. Shi, A. Karatzoglou, L. Baltrunas, M. Larson, N. Oliver, and A. Hanjalic. CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering. In Proc. of the sixth Recsys, 2012. Google ScholarDigital Library
- N. Tintarev and J. Masthoff. Designing and evaluating explanations for recommender systems. In Recommender Systems Handbook, pages 479--510. Springer, 2011.Google Scholar
- S. Vargas and P. Castells. Rank and relevance in novelty and diversity metrics for recommender systems. In Proceedings of the fifth ACM conference on Recommender systems, RecSys '11, pages 109--116, New York, NY, USA, 2011. ACM. Google ScholarDigital Library
- X. Yang, H. Steck, and Y. Liu. Circle-based recommendation in online social networks. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1267--1275. ACM, 2012. Google ScholarDigital Library
- Big & personal: data and models behind netflix recommendations
Recommendations
Discovering unknown but interesting items on personal social network
PAKDD'12: Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part IISocial networking service has become very popular recently. Many recommendation systems have been proposed to integrate with social networking websites. Traditional recommendation systems focus on providing popular items or items posted by close ...
Personalized recommendation based on the personal innovator degree
RecSys '09: Proceedings of the third ACM conference on Recommender systemsThis paper proposes a novel Collaborative Filtering scheme; it focuses on the dynamics and precedence of user preference to recommend items that match the latest preference of the target user. In predicting which items this user will purchase in the ...
Combining collaborative filtering with personal agents for better recommendations
AAAI '99/IAAI '99: Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligenceInformation filtering agents and collaborative filtering both attempt to alleviate information overload by identifying which items a user will find worthwhile. Information filtering (IF) focuses on the analysis of item content and the development of a ...
Comments