Statistical analysis with missing data
Statistical analysis with missing data
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Collaborative prediction using ensembles of Maximum Margin Matrix Factorizations
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
Matrix factorization and neighbor based algorithms for the netflix prize problem
Proceedings of the 2008 ACM conference on Recommender systems
Adaptive collaborative filtering
Proceedings of the 2008 ACM conference on Recommender systems
Missing data problems in machine learning
Missing data problems in machine learning
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Statistical models of music-listening sessions in social media
Proceedings of the 19th international conference on World wide web
Training and testing of recommender systems on data missing not at random
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Wisdom of the better few: cold start recommendation via representative based rating elicitation
Proceedings of the fifth ACM conference on Recommender systems
Multi-value probabilistic matrix factorization for IP-TV recommendations
Proceedings of the fifth ACM conference on Recommender systems
Proceedings of the fifth ACM international conference on Web search and data mining
Collaborative Filtering Recommender Systems
Foundations and Trends in Human-Computer Interaction
Gaussian process for recommender systems
KSEM'11 Proceedings of the 5th international conference on Knowledge Science, Engineering and Management
Towards mobile intelligence: Learning from GPS history data for collaborative recommendation
Artificial Intelligence
Recommender systems: from algorithms to user experience
User Modeling and User-Adapted Interaction
Using past-prediction accuracy in recommender systems
Information Sciences: an International Journal
Recommender systems: missing data and statistical model estimation
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Context relevance assessment and exploitation in mobile recommender systems
Personal and Ubiquitous Computing
Estimating conversion rate in display advertising from past erformance data
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
On top-k recommendation using social networks
Proceedings of the sixth ACM conference on Recommender systems
Proceedings of the sixth ACM conference on Recommender systems
User-centric evaluation of a K-furthest neighbor collaborative filtering recommender algorithm
Proceedings of the 2013 conference on Computer supported cooperative work
Rating Prediction by Correcting User Rating Bias
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Real time bid optimization with smooth budget delivery in online advertising
Proceedings of the Seventh International Workshop on Data Mining for Online Advertising
Evaluation of recommendations: rating-prediction and ranking
Proceedings of the 7th ACM conference on Recommender systems
Evaluating top-n recommendations "when the best are gone"
Proceedings of the 7th ACM conference on Recommender systems
Cost-Aware Collaborative Filtering for Travel Tour Recommendations
ACM Transactions on Information Systems (TOIS)
A behavioral perspective on social choice
Annals of Mathematics and Artificial Intelligence
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A fundamental aspect of rating-based recommender systems is the observation process, the process by which users choose the items they rate. Nearly all research on collaborative filtering and recommender systems is founded on the assumption that missing ratings are missing at random. The statistical theory of missing data shows that incorrect assumptions about missing data can lead to biased parameter estimation and prediction. In a recent study, we demonstrated strong evidence for violations of the missing at random condition in a real recommender system. In this paper we present the first study of the effect of non-random missing data on collaborative ranking, and extend our previous results regarding the impact of non-random missing data on collaborative prediction.