Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Restricted Boltzmann machines for collaborative filtering
Proceedings of the 24th international conference on Machine learning
Lessons from the Netflix prize challenge
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
I Like It... I Like It Not: Evaluating User Ratings Noise in Recommender Systems
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Efficient algorithms for ranking with SVMs
Information Retrieval
Temporal diversity in recommender systems
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
A stochastic learning-to-rank algorithm and its application to contextual advertising
Proceedings of the 20th international conference on World wide web
Rank and relevance in novelty and diversity metrics for recommender systems
Proceedings of the fifth ACM conference on Recommender systems
Online learning to diversify from implicit feedback
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Circle-based recommendation in online social networks
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering
Proceedings of the sixth ACM conference on Recommender systems
Mining large streams of user data for personalized recommendations
ACM SIGKDD Explorations Newsletter
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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.