Comparing Context-Aware Recommender Systems in Terms of Accuracy andDiversity: Which Contextual Modeling, Pre-filtering and Post-FilteringMethods Perform the Best

摘要

Although the area of Context-Aware Recommender Systems (CARS) has made asignificant progress over the last several years, the problem ofcomparing various contextual pre-filtering, post-filtering andcontextual modeling methods remained fairly unexplored. In this paper,we address this problem and compare several contextual pre-filtering,post-filtering and contextual modeling methods in terms of the accuracyand diversity of their recommendations to determine which methodsoutperform the others and under which circumstances. To this end, weconsider three major factors affecting performance of CARS methods, suchas the type of the recommendation task, context granularity and the typeof the recommendation data. We show that none of the considered CARSmethods uniformly dominates the others across all of these factors andother experimental settings; but that a certain group of contextualmodeling methods constitutes a reliable “best bet” whenchoosing a sound CARS approach since they provide a good balance ofaccuracy and diversity of contextual recommendations.