Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Gaussian Processes for Ordinal Regression
The Journal of Machine Learning Research
Collaborative ordinal regression
ICML '06 Proceedings of the 23rd international conference on Machine learning
Restricted Boltzmann machines for collaborative filtering
Proceedings of the 24th international conference on Machine learning
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Matchbox: large scale online bayesian recommendations
Proceedings of the 18th international conference on World wide web
BPR: Bayesian personalized ranking from implicit feedback
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
A Log-Linear Model with Latent Features for Dyadic Prediction
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Recommender Systems Handbook
Learning to rank social update streams
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
Co-factorization machines: modeling user interests and predicting individual decisions in Twitter
Proceedings of the sixth ACM international conference on Web search and data mining
Expert Systems with Applications: An International Journal
Mining large streams of user data for personalized recommendations
ACM SIGKDD Explorations Newsletter
Instant foodie: predicting expert ratings from grassroots
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Diffusion-aware personalized social update recommendation
Proceedings of the 7th ACM conference on Recommender systems
Retargeted matrix factorization for collaborative filtering
Proceedings of the 7th ACM conference on Recommender systems
Learning to rank for recommender systems
Proceedings of the 7th ACM conference on Recommender systems
A unified framework for reputation estimation in online rating systems
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Collaborative filtering on ordinal user feedback
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Modeling contextual agreement in preferences
Proceedings of the 23rd international conference on World wide web
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We propose a collaborative filtering (CF) recommendation framework, which is based on viewing user feedback on products as ordinal, rather than the more common numerical view. This way, we do not need to interpret each user feedback value as a number, but only rely on the more relaxed assumption of having an order among the different feedback ratings. Such an ordinal view frequently provides a more natural reflection of the user intention when providing qualitative ratings, allowing users to have different internal scoring scales. Moreover, we can address scenarios where assigning numerical scores to different types of user feedback would not be easy. Our approach is based on a pointwise ordinal model, which allows it to linearly scale with data size. The framework can wrap most collaborative filtering algorithms, upgrading those algorithms designed to handle numerical values into being able to handle ordinal values. In particular, we demonstrate our framework with wrapping a leading matrix factorization CF method. A cornerstone of our method is its ability to predict a full probability distribution of the expected item ratings, rather than only a single score for an item. One of the advantages this brings is a novel approach to estimating the confidence level in each individual prediction. Compared to previous approaches to confidence estimation, ours is more principled and empirically superior in its accuracy. We demonstrate the efficacy of the approach on some of the largest publicly available datasets, the Netflix data, and the Yahoo! Music data.