Predicting Neighbor Goodness in Collaborative Filtering

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Predicting Neighbor Goodness in Collaborative Filtering
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 subarea of Collaborative Filtering (CF), where it has barely been researched so far. We investigate the adaptation of clarity-based query performance predictors to define predictors of neighbor performance in CF. The proposed predictors are introduced in a memory-based CF algorithm to produce a dynamic variant where neighbor ratings are weighted based on their predicted performance. The approach is tested with encouraging empirical results, as the dynamic variants consistently outperform the baseline algorithms, with increasing difference on small neighborhoods.
Alejandro Bellogín, Pablo Castells
Added 26 May 2010
Updated 26 May 2010
Type Conference
Year 2009
Where FQAS
Authors Alejandro Bellogín, Pablo Castells
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