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An Approach to Controlling User Models and Personalization Effects in Recommender Systems

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TitleAn Approach to Controlling User Models and Personalization Effects in Recommender Systems
Publication TypeConference Paper
Year of Publication2013
AuthorsBakalov, F., M. - J. Meurs, B. König-Ries, B. Sateli, R. Witte, G. Butler, and A. Tsang
Refereed DesignationRefereed
Conference NameInternational Conference on Intelligent User Interfaces (IUI '13)
Pagination49–56
Date Published03/2013
PublisherACM
Conference LocationSanta Monica, CA, USA
Type of WorkPaper
ISBN Number978-1-4503-1965-2
Keywordsadaptive hypermedia, personalization, usability, user modeling
Abstract

Personalization nowadays is a commodity in a broad spectrum of computer systems. Examples range from online shops recommending products identified based on the user's previous purchases to web search engines sorting search hits based on the user browsing history. The aim of such adaptive behavior is to help users to find relevant content easier and faster. However, there are a number of negative aspects of this behavior. Adaptive systems have been criticized for violating the usability principles of direct manipulation systems, namely controllability, predictability, transparency, and unobtrusiveness. In this paper, we propose an approach to controlling adaptive behavior in recommender systems. It allows users to get an overview of personalization effects, view the user profile that is used for personalization, and adjust the profile and personalization effects to their needs and preferences. We present this approach using an example of a personalized portal for biochemical literature, whose users are biochemists, biologists and genomicists. Also, we report on a user study evaluating the impacts of controllable personalization on the usefulness, usability, user satisfaction, transparency, and trustworthiness of personalized systems.

URLhttp://dl.acm.org/authorize?6808714
DOI10.1145/2449396.2449405
Copyright

Copyright © ACM, 2013. This is the authors version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in IUI '13: Proceedings of the 2013 international conference on Intelligent user interfaces, http://doi.acm.org/10.1145/2449396.2449405.

Acceptance Rate

22%

Acknowledgments

We thank Justin Powlowski and all the participants of the user study for their time, patience, and valuable feedback. Funding for part of this work was provided by Genome Canada, Génome Québec and NSERC. Part of this research was sponsored by the IBM Ph.D. Fellowship Awards Program and the German Academic Exchange Service (DAAD).

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