Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
A support vector method for optimizing average precision
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
AdaRank: a boosting algorithm for information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
SoftRank: optimizing non-smooth rank metrics
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
EigenRank: a ranking-oriented approach to collaborative filtering
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Directly optimizing evaluation measures in learning to rank
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Information Retrieval
Introduction to Information Retrieval
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Regression-based latent factor models
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Expected reciprocal rank for graded relevance
Proceedings of the 18th ACM conference on Information and knowledge management
Probabilistic latent preference analysis for collaborative filtering
Proceedings of the 18th ACM conference on Information and knowledge management
A Survey of Accuracy Evaluation Metrics of Recommendation Tasks
The Journal of Machine Learning Research
BPR: Bayesian personalized ranking from implicit feedback
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Gradient descent optimization of smoothed information retrieval metrics
Information Retrieval
On statistical analysis and optimization of information retrieval effectiveness metrics
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Extending average precision to graded relevance judgments
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Performance of recommender algorithms on top-n recommendation tasks
Proceedings of the fourth ACM conference on Recommender systems
Collaborative competitive filtering: learning recommender using context of user choice
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Proceedings of the fifth ACM international conference on Web search and data mining
Distributed GraphLab: a framework for machine learning and data mining in the cloud
Proceedings of the VLDB Endowment
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
Personalized click shaping through lagrangian duality for online recommendation
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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
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Recommender systems are frequently used in domains in which users express their preferences in the form of graded judgments, such as ratings. Current ranking techniques are based on one of two sub-optimal approaches: either they optimize for a binary metric such as Average Precision, which discards information on relevance levels, or they optimize for Normalized Discounted Cumulative Gain (NDCG), which ignores the dependence of an item's contribution on the relevance of more highly ranked items. We address the shortcomings of existing approaches by proposing GAPfm, the Graded Average Precision factor model, which is a latent factor model for top-N recommendation in domains with graded relevance data. The model optimizes the Graded Average Precision metric that has been proposed recently for assessing the quality of ranked results lists for graded relevance. GAPfm's advantages are twofold: it maintains full information about graded relevance and also addresses the limitations of models that optimize NDCG. Experimental results show that GAPfm achieves substantial improvements on the top-N recommendation task, compared to several state-of-the-art approaches.