Utilizing Online Social Network and Location-Based Data to Recommend Items in an Online Marketplace

Recent research has unveiled the importance of online social networks for improving the quality of recommender systems and encouraged the research community to investigate better ways of exploiting the social information for recommendations. While most of the research focused on enhancing a traditional source of data (e.g., ratings, implicit feedback, or tags) with some type o f social information, little is known about how different sources of social data can be combined with other types of information relevant for recommendation. To contribute to this sparse field of resear ch, in this paper we exploit users’ interactions along three dimen sions of relevance (social, transactional, and location) to assess their performance in a barely studied domain: recommending items to people in an online marketplace environment. To that and we defined s ets of user similarity measures for each dimension of relevance and studied them isolated and in combination via hybrid recommender approaches, to assess which one provides the best recommendation performance. Interestingly, in our experiments conducted on a rich dataset collected from SecondLife, a popular online virtual world, we found that recommenders relying on similarity measures obtained from the social network yielded better results than those inferred directly from the marketplace data.