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Recent research has unveiled the importance of online social networks forimproving the quality of recommender systems and encouraged the researchcommunity to investigate better ways of exploiting the social information forrecommendations. To contribute to this sparse field of research, in this paperwe exploit users' interactions along three data sources (marketplace, socialnetwork and location-based) to assess their performance in a barely studieddomain: recommending products and domains of interests (i.e., productcategories) to people in an online marketplace environment. To that end wedefined sets of content- and network-based user similarity features for eachdata source and studied them isolated using an user-based CollaborativeFiltering (CF) approach and in combination via a hybrid recommender algorithm,to assess which one provides the best recommendation performance.Interestingly, in our experiments conducted on a rich dataset collected fromSecondLife, a popular online virtual world, we found that recommenders relyingon user similarity features obtained from the social network data clearlyyielded the best results in terms of accuracy in case of predicting products,whereas the features obtained from the marketplace and location-based datasources also obtained very good results in case of predicting categories. Thisfinding indicates that all three types of data sources are important and shouldbe taken into account depending on the level of specialization of therecommendation task.

Utilizing Online Social Network and Location-Based Data to Recommend Products and Categories in Online Marketplaces