14th International World Wide Web Conference (WWW '05)
May 10-14, 2005, Chiba, Japan.
Improving Recommendation Lists Through Topic Diversification
Sean M. McNee,
Joseph A. Konstan,
In this work we present topic diversification, a novel method
designed to balance and diversify personalized recommendation
lists in order to reflect the user's entire spectrum of interests.
Though being detrimental to average accuracy, we show that our
method improves user satisfaction with recommendation lists, in
particular for lists generated using the common item-based
collaborative filtering algorithm.
Our work builds upon prior research on recommender systems,
looking at properties of recommendation lists as entities in their
own right rather than specifically focusing on the accuracy of
individual recommendations. We introduce the intra-list similarity
metric to assess the topical diversity of recommendation lists and
the topic diversification approach for decreasing intra-list
similarity. We evaluate our method using book recommendation data,
including offline analysis on 361,349 ratings and an online
study involving more than 2,100 subjects.