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The value of user’s Facebook profile data for product recommendation generation


Most online shops apply recommender systems, i.e. software agents that elicit the users’ preferences and interests with the purpose to make product recommendations. Many of these systems suffer from the new user cold start problem which occurs when no transaction history is available for the particular new prospective buyer. External data from social networking sites, like Facebook, seem promising to overcome this problem. In this paper, we evaluate the value of Facebook profile data to create meaningful product recommendations. We find based on the outcomes of a user experiment that already simple approaches and plain profile data matching yield significant better recommendations than a pure random draw from the product data base. However, the most successful approaches use semantic categories like music/video, brands and product category information to match profile and product data. A second experiment indicates that recommendation quality seems to be stable for different profile sizes.

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  2. Stop words are common words defined as irrelevant for a search (e.g. “a”, “and”, “the” etc.), cf. e.g. (Jannach et al. 2010, p. 56)


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The authors would like to thank the seminar participants and students involved in this project. Further, the authors are grateful for all comments and suggestions they received, in particular by the reviewers and audience at ECIS 2013 where we presented parts of this work.

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Correspondence to Irina Heimbach.

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Heimbach, I., Gottschlich, J. & Hinz, O. The value of user’s Facebook profile data for product recommendation generation. Electron Markets 25, 125–138 (2015).

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  • Product recommendation
  • Cold start problem
  • Recommender
  • Facebook
  • Social shopping sites
  • Jell Classification
  • C90
  • M31