Long-term and session-specific user preferences in a mobile recommender system

  • Authors:
  • Quang Nhat Nguyen;Francesco Ricci

  • Affiliations:
  • Free University of Bozen-Bolzano, Bolzano, Italy;Free University of Bozen-Bolzano, Bolzano, Italy

  • Venue:
  • Proceedings of the 13th international conference on Intelligent user interfaces
  • Year:
  • 2008

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Abstract

User preferences acquisition plays a very important role for recommender systems. In a previous paper, we proposed a critique-based mobile recommendation methodology exploiting both long-term and session-specific user preferences. In this paper, we evaluate the impact on the recommendation accuracy of the two kinds of user preferences. We have ran off-line experiments exploiting the log data recorded in a previous live-user evaluation, and we show here that exploiting both long-term and session-specific preferences results in a better recommendation accuracy than using a single user model component. Moreover, we show that when the simulated user behavior deviates from that dictated by the acquired user model the session-specific preferences are more useful than the long-term ones in predicting user decisions.