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Item-Based Explanations for User-Based Recommendations

Authors:
Marius Kaminskas
Fred Durao
Derek Bridge

Keywords: Recommender systems; Explanations; Collaborative filtering.

Abstract:
Explanations can increase user satisfaction with recommender systems. While it is relatively easy to explain the recommendations of a content-based or an item-based collaborative recommender system, user-based collaborative recommendations are harder to explain. In this work, we adopt an approach from the literature that generates explanation rules for user-based collaborative-filtering recommendations. These rules are item-based: for example, ``If you liked Toy Story then you might also like Finding Nemo''. We modify the approach by proposing two new, alternative measures of explanation rule quality. We evaluate the two new measures in a user study and show that users prefer explanation rules whose antecedents are both accurate and unique with respect to the recommended item.

Pages: 65 to 70

Copyright: Copyright (c) IARIA, 2017

Publication date: March 19, 2017

Published in: conference

ISSN: 2308-4375

ISBN: 978-1-61208-542-5

Location: Nice, France

Dates: from March 19, 2017 to March 23, 2017