The importance of being dissimilar in Recommendation

Proceedings of the 34rd Annual ACM Symposium on Applied Computing, SAC 2019, Limassol, Cyprus April 8-12 2019 - 2019
Download the publication : SAC2019.pdf [680Ko]  
In recommendation scenarios, similarity measures play a fundamental role in memory-based nearest neighbors approaches. In fact, they recommend items to a user based on the similarity of either items or users in a neighborhood. In this paper, we argue that similarity between users or items, although it keeps leading importance in computing recommendations, should be paired with a value of dissimilarity (computed not just as the complement of the similarity one). We formally modeled and injected this notion in some of the most used similarity measures and evaluated our approach in a recommendation scenario showing its effectiveness with respect to accuracy and diversity results on three different datasets.

BibTex references


@InProceedings{ADDRT19,
author = {Vito Walter Anelli and Tommaso {Di Noia} and Eugenio {Di Sciascio} and Azzurra Ragone and Joseph Trotta},
title = "The importance of being dissimilar in
Recommendation",
booktitle = "Proceedings of the 34rd Annual ACM Symposium on
Applied Computing, SAC 2019, Limassol, Cyprus
April 8-12 2019",
year = "2019",
publisher = "ACM Press",
note = "to appear",
url = "http://sisinflab.poliba.it/publications/2019/ADDRT
19"
}

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