SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Proceedings of the 10th international conference on Intelligent user interfaces
IEEE Transactions on Knowledge and Data Engineering
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
Query performance prediction in web search environments
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Lessons from the Netflix prize challenge
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
Locally Adaptive Neighborhood Selection for Collaborative Filtering Recommendations
AH '08 Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Query hardness estimation using Jensen-Shannon divergence among multiple scoring functions
ECIR'07 Proceedings of the 29th European conference on IR research
Self-adjusting hybrid recommenders based on social network analysis
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Predicting the performance of recommender systems: an information theoretic approach
ICTIR'11 Proceedings of the Third international conference on Advances in information retrieval theory
A performance prediction approach to enhance collaborative filtering performance
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Dynamic generation of personalized hybrid recommender systems
Proceedings of the 7th ACM conference on Recommender systems
| Hi-index | 0.00 |
Performance prediction has gained growing attention in the Information Retrieval field since the late nineties and has become an established research topic in the field. Our work restates the problem in the area of Recommender Systems, where it has barely been researched so far, despite being an appealing problem, as it enables an array of strategies for deciding when to deliver or hold back recommendations based on their foreseen accuracy. We investigate the adaptation and definition of different performance predictors based on the available user and item features. The properties of the predictor are empirically studied by checking the correlation of the predictor output with a performance measure. Then, we propose to introduce the performance predictor in a recommender system to produce a dynamic strategy. Depending on how the predictor is introduced we analyze two different problems: dynamic neighbor weighting in collaborative filtering and dynamic weighting of ensemble recommenders.