User Modeling, Adaptation, and Personalization

Volume 5535 of the series Lecture Notes in Computer Science pp 295-306

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

  • Nicolas DucheneautAffiliated withPalo Alto Research Center
  • , Kurt PartridgeAffiliated withPalo Alto Research Center
  • , Qingfeng HuangAffiliated withPalo Alto Research Center
  • , Bob PriceAffiliated withPalo Alto Research Center
  • , Mike RobertsAffiliated withPalo Alto Research Center
  • , Ed H. ChiAffiliated withPalo Alto Research Center
  • , Victoria BellottiAffiliated withPalo Alto Research Center
  • , Bo BegoleAffiliated withPalo Alto Research Center

* Final gross prices may vary according to local VAT.

Get Access


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.


Recommender systems hybrid models evaluation