GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Combining fuzzy information from multiple systems (extended abstract)
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Fab: content-based, collaborative recommendation
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Combining collaborative filtering with personal agents for better recommendations
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Feature Matrices: A Model for Efficient and Anonymous Web Usage Mining
EC-Web 2001 Proceedings of the Second International Conference on Electronic Commerce and Web Technologies
Knowledge discovery from users Web-page navigation
RIDE '97 Proceedings of the 7th International Workshop on Research Issues in Data Engineering (RIDE '97) High Performance Database Management for Large-Scale Applications
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A Framework for Efficient and Anonymous Web Usage Mining Based on Client-Side Tracking
WEBKDD '01 Revised Papers from the Third International Workshop on Mining Web Log Data Across All Customers Touch Points
Personalization of Queries in Database Systems
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Personalized Queries under a Generalized Preference Model
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Towards a journalist-based news recommendation system: The Wesomender approach
Expert Systems with Applications: An International Journal
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Recommendation systems are applied to personalize and customize the Web environment. We have developed a recommendation system, termed Yoda, that is designed to support large-scale Web-based applications requiring highly accurate recommendations in real-time. With Yoda, we introduce a hybrid approach that combines collaborative filtering (CF) and content-based querying to achieve higher accuracy. Yoda is structured as a tunable model that is trained off-line and employed for real-time recommendation on-line. The on-line process benefits from an optimized aggregation function with low complexity that allows realtime weighted aggregation of the soft classification of active users to predefined recommendation sets. Leveraging on localized distribution of the recommendable items, the same aggregation function is further optimized for the off-line process to reduce the time complexity of constructing the pre-defined recommendation sets of the model. To make the off-line process scalable furthermore, we also propose a filtering mechanism, FLSH, that extends the Locality Sensitive Hashing technique by incorporating a novel distance measure that satisfies specific requirements of our application. Our end-to-end experiments show while Yoda's complexity is low and remains constant as the number of users and/or items grow, its accuracy surpasses that of the basic nearest-neighbor method by a wide margin (in most cases more than 100%).