SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Using temporal profiles of queries for precision prediction
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 10th international conference on Intelligent user interfaces
IEEE Transactions on Knowledge and Data Engineering
Scalable collaborative filtering using cluster-based smoothing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
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
Precision prediction based on ranked list coherence
Information Retrieval
Ranking robustness: a novel framework to predict query performance
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
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
Relevance models for topic detection and tracking
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Locally Adaptive Neighborhood Selection for Collaborative Filtering Recommendations
AH '08 Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Multidimensional credibility model for neighbor selection in collaborative recommendation
Expert Systems with Applications: An International Journal
The Combination and Evaluation of Query Performance Prediction Methods
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Query hardness estimation using Jensen-Shannon divergence among multiple scoring functions
ECIR'07 Proceedings of the 29th European conference on IR research
Performance prediction in recommender systems
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Predicting the performance of recommender systems: an information theoretic approach
ICTIR'11 Proceedings of the Third international conference on Advances in information retrieval theory
Predicting performance in recommender systems
Proceedings of the fifth ACM conference on Recommender systems
Recent developments in information retrieval
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Stochastic search for global neighbors selection in collaborative filtering
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Proceedings of the 27th Annual ACM Symposium on Applied Computing
From neighbors to global neighbors in collaborative filtering: an evolutionary optimization approach
Proceedings of the 14th annual conference on Genetic and evolutionary computation
ACM Transactions on the Web (TWEB)
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Performance prediction has gained increasing attention in the IR field since the half of the past decade and has become an established research topic in the field. The present work restates the problem in the area of Collaborative Filtering (CF), where it has barely been researched so far. We investigate the adaptation of clarity-based query performance predictors to predict neighbor performance in CF. A predictor is proposed and introduced in a kNN CF algorithm to produce a dynamic variant where neighbor ratings are weighted based on their predicted performance. The properties of the predictor are empirically studied by, first, checking the correlation of the predictor output with a proposed measure of neighbor performance. Then, the performance of the dynamic kNN variant is examined on different sparsity and neighborhood size conditions, where the variant consistently outperforms the baseline algorithm, with increasing difference on small neighborhoods.