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Analysis of Performance Improvement of Collaborative Filtering based on Neighbor Selection Criteria  

Lee, Soojung (경인교육대학교)
Publication Information
The Journal of Korean Association of Computer Education / v.18, no.4, 2015 , pp. 55-62 More about this Journal
Abstract
Recommender systems through collaborative filtering has been utilized successfully in various areas by providing with convenience in searching information. Measuring similarity is critical in determining performance of these systems, because it is the criteria for the range of recommenders. This study analyzes distributions of similarity from traditional measures and investigates relations between similarities and the number of co-rated items. With this, this study suggests a method for selecting reliable recommenders by restricting similarities, which compensates for the drawbacks of previous measures. Experimental results showed that restricting similarities of neighbors by upper and lower thresholds yield superior performance than previous methods, especially when consulting fewer nearest neighbors. Maximum improvement of 0.047 for cosine similarity and that of 0.03 for Pearson was achieved. This result tells that a collaborative filtering system using Pearson or cosine similarities should not consult neighbors with very high or low similarities.
Keywords
Collaborative Filtering; Recommender System; Similarity Measure;
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Times Cited By KSCI : 1  (Citation Analysis)
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