Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Nantonac collaborative filtering: recommendation based on order responses
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Evaluating similarity measures: a large-scale study in the orkut social network
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search
ACM Transactions on Information Systems (TOIS)
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Learning to rank: from pairwise approach to listwise approach
Proceedings of the 24th international conference on Machine learning
An experimental comparison of click position-bias models
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Combinational collaborative filtering for personalized community recommendation
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Presentation bias is significant in determining user preference for search results—A user study
Journal of the American Society for Information Science and Technology
One-Class Collaborative Filtering
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Collaborative filtering for orkut communities: discovery of user latent behavior
Proceedings of the 18th international conference on World wide web
Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Probabilistic latent preference analysis for collaborative filtering
Proceedings of the 18th ACM conference on Information and knowledge management
BPR: Bayesian personalized ranking from implicit feedback
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Which photo groups should I choose? A comparative study of recommendation algorithms in Flickr
Journal of Information Science
Two of a Kind or the Ratings Game? Adaptive Pairwise Preferences and Latent Factor Models
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Ranking from pairs and triplets: information quality, evaluation methods and query complexity
Proceedings of the fourth ACM international conference on Web search and data mining
Learning to Rank for Information Retrieval and Natural Language Processing
Learning to Rank for Information Retrieval and Natural Language Processing
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Many online systems present a list of recommendations and infer user interests implicitly from clicks or other contextual actions. For modeling user feedback in such settings, a common approach is to consider items acted upon to be relevant to the user, and irrelevant otherwise. However, clicking some but not others conveys an implicit ordering of the presented items. Pairwise learning, which leverages such implicit ordering between a pair of items, has been successful in areas such as search ranking. In this work, we study whether pairwise learning can improve community recommendation. We first present two novel pairwise models adapted from logistic regression. Both offline and online experiments in a large real-world setting show that incorporating pairwise learning improves the recommendation performance. However, the improvement is only slight. We find that users' preferences regarding the kinds of communities they like can differ greatly, which adversely affect the effectiveness of features derived from pairwise comparisons. We therefore propose a probabilistic latent semantic indexing model for pairwise learning (Pairwise PLSI), which assumes a set of users' latent preferences between pairs of items. Our experiments show favorable results for the Pairwise PLSI model and point to the potential of using pairwise learning for community recommendation.