If you liked Herlocker et al.’s explanations paper, then you might like this paper too
Refereed Conference Meeting Proceeding
We present explanation rules, which provide explanations of user-based collaborative recommendations but in a form that is familiar from item-based collaborative recommendations; for example, “People who liked Toy Story also like Finding Nemo”. We present an algorithm for computing explanation rules. We report the results of a web-based user trial that gives a preliminary evaluation of the perceived effectiveness of explanation rules. In particular, we find that nearly 50% of participants found this style of explanation to be helpful, and nearly 80% of participants who expressed a preference found explanation rules to be more helpful than similar rules that were closely-related but partly-random.
Workshop on Interfaces and Human Decision Making for Recommender Systems at ACM RecSys 2014
Digital Object Identifer (DOI):
United States of America
National University of Ireland, Cork (UCC)
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