Collaborative Filtering Is Not Enough? Experiments with a Mixed-Model Recommender for Leisure Activities

  • Nicolas Ducheneaut
  • Kurt Partridge
  • Qingfeng Huang
  • Bob Price
  • Mike Roberts
  • Ed H. Chi
  • Victoria Bellotti
  • Bo Begole
Conference paper

DOI: 10.1007/978-3-642-02247-0_28

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5535)
Cite this paper as:
Ducheneaut N. et al. (2009) Collaborative Filtering Is Not Enough? Experiments with a Mixed-Model Recommender for Leisure Activities. In: Houben GJ., McCalla G., Pianesi F., Zancanaro M. (eds) User Modeling, Adaptation, and Personalization. UMAP 2009. Lecture Notes in Computer Science, vol 5535. Springer, Berlin, Heidelberg

Abstract

Collaborative filtering (CF) is at the heart of most successful recommender systems nowadays. While this technique often provides useful recommendations, conventional systems also ignore data that could potentially be used to refine and adjust recommendations based on a user’s context and preferences. The problem is particularly acute with mobile systems where information delivery often needs to be contextualized. Past research has also shown that combining CF with other techniques often improves the quality of recommendations. In this paper, we present results from an experiment assessing user satisfaction with recommendations for leisure activities that are obtained from different combinations of these techniques. We show that the most effective mix is highly dependent on a user’s familiarity with a geographical area and discuss the implications of our findings for future research.

Keywords

Recommender systems hybrid models evaluation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Nicolas Ducheneaut
    • 1
  • Kurt Partridge
    • 1
  • Qingfeng Huang
    • 1
  • Bob Price
    • 1
  • Mike Roberts
    • 1
  • Ed H. Chi
    • 1
  • Victoria Bellotti
    • 1
  • Bo Begole
    • 1
  1. 1.Palo Alto Research CenterPalo AltoUSA

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