A Probabilistic Approach for Mining Drifting User Interest
APWeb/WAIM '09 Proceedings of the Joint International Conferences on Advances in Data and Web Management
TANGENT: a novel, 'Surprise me', recommendation algorithm
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A document recommendation system based on clustering P2P networks
CDVE'07 Proceedings of the 4th international conference on Cooperative design, visualization, and engineering
A collaborative recommender system based on asymmetric user similarity
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Beyond accuracy: evaluating recommender systems by coverage and serendipity
Proceedings of the fourth ACM conference on Recommender systems
Personalized recommendation based on partial similarity of interests
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Towards discovery of subgraph bisociations
Bisociative Knowledge Discovery
| Hi-index | 0.01 |
Current collaborative filtering can't represent various aspects of users' interests. We propose a recommendation method in which a user can find new interests that are partially similar to the user's taste. Partial similarity is an aspect of the user's preference which is projected by the community in which the user belongs. We developed a television program recommendation system which performs such recommendation with serendipity, conducted an actual experiment and evaluated its results.