Personalized information delivery: an analysis of information filtering methods
Communications of the ACM - Special issue on information filtering
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Communications of the ACM
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior
Proceedings of the 2nd ACM conference on Electronic commerce
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Meta-recommendation systems: user-controlled integration of diverse recommendations
Proceedings of the eleventh international conference on Information and knowledge management
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Fuzzy logic methods in recommender systems
Fuzzy Sets and Systems - Theme: Multicriteria decision
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Informed Recommender: Basing Recommendations on Consumer Product Reviews
IEEE Intelligent Systems
Modeling relationships at multiple scales to improve accuracy of large recommender systems
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Lessons from the Netflix prize challenge
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
Feature weighting in content based recommendation system using social network analysis
Proceedings of the 17th international conference on World Wide Web
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Factor in the neighbors: Scalable and accurate collaborative filtering
ACM Transactions on Knowledge Discovery from Data (TKDD)
EmotiBlog: a finer-grained and more precise learning of subjectivity expression models
LAW IV '10 Proceedings of the Fourth Linguistic Annotation Workshop
We feel fine and searching the emotional web
Proceedings of the fourth ACM international conference on Web search and data mining
Large-scale matrix factorization with distributed stochastic gradient descent
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
An information theoretic approach to sentiment polarity classification
Proceedings of the 2nd Joint WICOW/AIRWeb Workshop on Web Quality
Predicting IMDB movie ratings using social media
ECIR'12 Proceedings of the 34th European conference on Advances in Information Retrieval
Review and functional classification of collaborative systems
International Journal of Information Management: The Journal for Information Professionals
Sentic Computing: Techniques, Tools, and Applications
Sentic Computing: Techniques, Tools, and Applications
Robustness analysis of privacy-preserving model-based recommendation schemes
Expert Systems with Applications: An International Journal
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This work presents a novel application of Sentiment Analysis in Recommender Systems by categorizing users according to the average polarity of their comments. These categories are used as attributes in Collaborative Filtering algorithms. To test this solution a new corpus of opinions on movies obtained from the Internet Movie Database (IMDb) has been generated, so both ratings and comments are available. The experiments stress the informative value of comments. By applying Sentiment Analysis approaches some Collaborative Filtering algorithms can be improved in rating prediction tasks. The results indicate that we obtain a more reliable prediction considering only the opinion text (RMSE of 1.868), than when apply similarities over the entire user community (RMSE of 2.134) and sentiment analysis can be advantageous to recommender systems.