The Value Of Users' Facebook Profile Data - Generating Product Recommendations For Online Social Shopping Sites

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 an 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 best recommendations are based on music/video, brands and product/category information that need to be extracted from the Facebook profile with more sophisticated approaches.