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Comparing context-aware recommender systems in terms of accuracy and diversity


Although the area of context-aware recommender systems (CARS) has made a significant progress over the last several years, the problem of comparing various contextual pre-filtering, post-filtering and contextual 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 accuracy and diversity of their recommendations to determine which methods outperform the others and under which circumstances. To this end, we consider three major factors affecting performance of CARS methods, such as the type of the recommendation task, context granularity and the type of the recommendation data. We show that none of the considered CARS methods uniformly dominates the others across all of these factors and other experimental settings; but that a certain group of contextual modeling methods constitutes a reliable “best bet” when choosing a sound CARS approach since they provide a good balance of accuracy and diversity of contextual recommendations.

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  1. The Tidemann-Hall index is very similar to the Rosenbluth index, the only difference between them is that they rank categories differently. The categories are ranked in ascending order in the Rosenbluth index and in descending order in the Tidemann-Hall index. We decided to use the Tidemann-Hall index since our aim was to compare datasets with different categories (Meilak 2008) and the Tidemann-Hall is better suited for this task than the Rosenbluth index because it is more sensitive to the absolute number of categories (the largest category receives a weight equal to one in the formula).


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Correspondence to Michele Gorgoglione.

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Panniello, U., Tuzhilin, A. & Gorgoglione, M. Comparing context-aware recommender systems in terms of accuracy and diversity. User Model User-Adap Inter 24, 35–65 (2014).

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  • Context-aware recommender systems
  • CARS
  • Pre-filtering
  • Post-filtering
  • Contextual modeling
  • Accuracy
  • Diversity
  • Performance measures