GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Latent Class Models for Collaborative Filtering
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Journal of the American Society for Information Science and Technology
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
An automatic weighting scheme for collaborative filtering
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Scalable collaborative filtering using cluster-based smoothing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
A study of mixture models for collaborative filtering
Information Retrieval
Unifying user-based and item-based collaborative filtering approaches by similarity fusion
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
An Inference-based Collaborative Filtering Approach
DASC '07 Proceedings of the Third IEEE International Symposium on Dependable, Autonomic and Secure Computing
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Preference-based graphic models for collaborative filtering
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
A user-item relevance model for log-based collaborative filtering
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
Modding as Rating Behavior in Virtual Communities: The Case of Rooster Teeth Productions
OCSC '09 Proceedings of the 3d International Conference on Online Communities and Social Computing: Held as Part of HCI International 2009
A new collaborative filtering metric that improves the behavior of recommender systems
Knowledge-Based Systems
FlowWiki: A wiki based platform for ad hoc collaborative workflows
Knowledge-Based Systems
A new feature selection algorithm based on binomial hypothesis testing for spam filtering
Knowledge-Based Systems
A case-based knowledge system for safety evaluation decision making of thermal power plants
Knowledge-Based Systems
A collaborative filtering approach to mitigate the new user cold start problem
Knowledge-Based Systems
Incremental Collaborative Filtering recommender based on Regularized Matrix Factorization
Knowledge-Based Systems
A careful assessment of recommendation algorithms related to dimension reduction techniques
Knowledge-Based Systems
Interest-based real-time content recommendation in online social communities
Knowledge-Based Systems
A literature review and classification of recommender systems research
Expert Systems with Applications: An International Journal
Applying the learning rate adaptation to the matrix factorization based collaborative filtering
Knowledge-Based Systems
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In collaborative filtering, the existing memory-based methods make recommendations based on the overall consistency between two users or two items. The major concerns with these methods are: (1) they are sometimes being overly confident; (2) they are prone to disregard some useful information in the user profiles; (3) they often imply some untrustworthy inferences in making a prediction. This work investigates the drawbacks of these methods, and then proposes a collaborative filtering approach based on heuristic formulated inferences. The proposed approach is based on the fact that any two users may have some common interest genres as well as different ones. Different from most existing methods, this approach introduces a more reasonable similarity measure metric, considers users' preferences and rating patterns, and promotes rational individual prediction, thus more comprehensively measures the relevance between user and item. Experimental results from two popular public datasets show that the proposed approach improves the prediction quality significantly over several other popular methods.