Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Collaborative filtering via gaussian probabilistic latent semantic analysis
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Journal of Machine Learning Research
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
A maximum entropy web recommendation system: combining collaborative and content features
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Being accurate is not enough: how accuracy metrics have hurt recommender systems
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo
Proceedings of the 25th international conference on Machine learning
An Evaluation Methodology for Collaborative Recommender Systems
AXMEDIS '08 Proceedings of the 2008 International Conference on Automated solutions for Cross Media Content and Multi-channel Distribution
Matchbox: large scale online bayesian recommendations
Proceedings of the 18th international conference on World wide web
Latent class models for collaborative filtering
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
fLDA: matrix factorization through latent dirichlet allocation
Proceedings of the third ACM international conference on Web search and data mining
Content-based recommendation systems
The adaptive web
Performance of recommender algorithms on top-n recommendation tasks
Proceedings of the fourth ACM conference on Recommender systems
Beyond accuracy: evaluating recommender systems by coverage and serendipity
Proceedings of the fourth ACM conference on Recommender systems
Generalized Probabilistic Matrix Factorizations for Collaborative Filtering
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Modeling item selection and relevance for accurate recommendations: a bayesian approach
Proceedings of the fifth ACM conference on Recommender systems
Cascade-based community detection
Proceedings of the sixth ACM international conference on Web search and data mining
App recommendation: a contest between satisfaction and temptation
Proceedings of the sixth ACM international conference on Web search and data mining
Learning User Preference Patterns for Top-N Recommendations
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Probabilistic topic models for sequence data
Machine Learning
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In this work we perform an analysis of probabilistic approaches to recommendation upon a different validation perspective, which focuses on accuracy metrics such as recall and precision of the recommendation list. Traditionally, state-of-art approches to recommendations consider the recommendation process from a "missing value prediction" perspective. This approach simplifies the model validation phase that is based on the minimization of standard error metrics such as RMSE. However, recent studies have pointed several limitations of this approach, showing that a lower RMSE does not necessarily imply improvements in terms of specific recommendations. We demonstrate that the underlying probabilistic framework offers several advantages over traditional methods, in terms of flexibility in the generation of the recommendation list and consequently in the accuracy of recommendation.