Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
IEEE Transactions on Knowledge and Data Engineering
Learning diverse rankings with multi-armed bandits
Proceedings of the 25th international conference on Machine learning
Introduction to Information Retrieval
Introduction to Information Retrieval
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
BPR: Bayesian personalized ranking from implicit feedback
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
OrdRec: an ordinal model for predicting personalized item rating distributions
Proceedings of the fifth ACM conference on Recommender systems
TFMAP: optimizing MAP for top-n context-aware recommendation
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering
Proceedings of the sixth ACM conference on Recommender systems
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Recommender system aim at providing a personalized list of items ranked according to the preferences of the user, as such ranking methods are at the core of many recommendation algorithms. The topic of this tutorial focuses on the cutting-edge algorithmic development in the area of recommender systems. This tutorial will provide an in depth picture of the progress of ranking models in the field, summarizing the strengths and weaknesses of existing methods, and discussing open issues that could be promising for future research in the community. A qualitative and quantitative comparison between different models will be provided while we will also highlight recent developments in the areas of Reinforcement Learning.