Fab: content-based, collaborative recommendation
Communications of the ACM
An Evaluation of Neighbourhood Formation on the Performance of Collaborative Filtering
Artificial Intelligence Review
Taxonomy-driven computation of product recommendations
Proceedings of the thirteenth ACM international conference on Information and knowledge management
IEEE Transactions on Knowledge and Data Engineering
A Scalable Collaborative Filtering Framework Based on Co-Clustering
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
HIS '07 Proceedings of the 7th International Conference on Hybrid Intelligent Systems
Classification-based collaborative filtering using market basket data
Expert Systems with Applications: An International Journal
Improving the scalability of recommender systems by clustering using genetic algorithms
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Behavioral cost-based recommendation model for wanderers in town
HCII'11 Proceedings of the 14th international conference on Human-computer interaction: towards mobile and intelligent interaction environments - Volume Part III
Personalization in tag ontology learning for recommendation making
Proceedings of the 14th International Conference on Information Integration and Web-based Applications & Services
Bringing knowledge into recommender systems
Journal of Systems and Software
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Recommender systems offer personalization to online activities due to their ability to recommend products that are unknown to the user. The most common form of these systems employs collaborative filtering to make recommendations and operate by estimating a preference for an item based on how like minded users have previously rated items. Such methods require large amounts of training data which highlights a scalability problem of collaborative filtering, namely, the trade-off between accurate estimation prediction and the time required to calculate them. This paper demonstrates a novel approach to determine interest thus improving scalability by partitioning training data into user based profile clusters. The partitioned data represents user segments (or profile types) which is used to as a more concise representation of similar users for the target. Experimental results have shown a dramatic increase in prediction speed without a loss in accuracy.