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How users perceive and appraise personalized recommendations

By Nicolas Jones, Pearl Pu and Li Chen


Abstract. Traditional websites have long relied on users revealing their preferences explicitly through direct manipulation interfaces. However recent recommender systems have gone as far as using implicit feedback indicators to understand users ’ interests. More than a decade after the emergence of recommender systems, the question whether users prefer them compared to stating their preferences explicitly, largely remains a subject of study. Even though some studies were found on users ’ acceptance and perceptions of this technology, these were general marketing-oriented surveys. In this paper we report an in-depth user study comparing Amazon’s implicit book recommender with a baseline model of explicit search and browse. We address not only the question “do people accept recommender systems ” but also how or under what circumstances they do and more importantly, what can still be improved. 1 Introduction and Related Work Twenty years ago, the classical buying-scheme was that when a user entered a shop, a knowledgeable seller would be available to advise and inform him/her on products

Publisher: Springer-Verlag
Year: 2009
OAI identifier: oai:CiteSeerX.psu:
Provided by: CiteSeerX
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