A Performance Prediction Approach to Enhance Collaborative Filtering Performance

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.