GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
The nature of statistical learning theory
The nature of statistical learning theory
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
An Efficient Boosting Algorithm for Combining Preferences
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Information Filtering: Overview of Issues, Research and Systems
User Modeling and User-Adapted Interaction
Towards a More Comprehensive Comparison of Collaborative Filtering Algorithms
SPIRE 2002 Proceedings of the 9th International Symposium on String Processing and Information Retrieval
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
An image retrieval method based on analysis of feedback sequence log
Pattern Recognition Letters
Collaborative filtering with decoupled models for preferences and ratings
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
A comparison of several predictive algorithms for collaborative filtering on multi-valued ratings
Proceedings of the 2004 ACM symposium on Applied computing
A Maximum Entropy Approach for Collaborative Filtering
Journal of VLSI Signal Processing Systems
An automatic weighting scheme for collaborative filtering
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
An Accurate and Scalable Collaborative Recommender
Artificial Intelligence Review
Scalable collaborative filtering using cluster-based smoothing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Neural Networks - 2004 Special issue: New developments in self-organizing systems
A study of mixture models for collaborative filtering
Information Retrieval
A probabilistic music recommender considering user opinions and audio features
Information Processing and Management: an International Journal - Special issue: AIRS2005: Information retrieval research in Asia
User language model for collaborative personalized search
ACM Transactions on Information Systems (TOIS)
Collaborative filtering using orthogonal nonnegative matrix tri-factorization
Information Processing and Management: an International Journal
An empirical study of data smoothing methods for memory-based and hybrid collaborative filtering
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Collaborative filtering via euclidean embedding
Proceedings of the fourth ACM conference on Recommender systems
Exploiting rating behaviors for effective collaborative filtering
WISE'06 Proceedings of the 7th international conference on Web Information Systems
Web usage mining using evolutionary support vector machine
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
A probabilistic model for music recommendation considering audio features
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
Collaborative filtering via temporal euclidean embedding
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
Transfer learning in heterogeneous collaborative filtering domains
Artificial Intelligence
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We present a Java-based framework, SWAMI (Shared Wisdom through the Amalgamation of Many Interpretations) for building and studying collaborative filtering systems. SWAMI consists of three components: a prediction engine, an evaluation system, and a visualization component. The prediction engine provides a common interface for implementing different prediction algorithms. The evaluation system provides a standardized testing methodology and metrics for analyzing the accuracy and run-time performance of prediction algorithms. The visualization component suggests how graphical representations can inform the development and analysis of prediction algorithms. We demonstrate SWAMI on the Each Movie data set by comparing three prediction algorithms: a traditional Pearson correlation-based method, support vector machines, and a new accurate and scalable correlation-based method based on clustering techniques.