Utilizing Online Social Network and Location-Based Data to Recommend Products and Categories in Online Marketplaces
This work addresses improving recommender systems for online marketplace users by integrating multiple data sources, though it is incremental as it builds on existing collaborative filtering and hybrid methods.
The paper tackled the problem of recommending products and categories in online marketplaces by exploiting user interactions from marketplace, social network, and location-based data sources, finding that social network data yielded the best accuracy for product recommendations while all three sources were important for category recommendations.
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. To contribute to this sparse field of research, in this paper we exploit users' interactions along three data sources (marketplace, social network and location-based) to assess their performance in a barely studied domain: recommending products and domains of interests (i.e., product categories) to people in an online marketplace environment. To that end we defined sets of content- and network-based user similarity features for each data source and studied them isolated using an user-based Collaborative Filtering (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 from SecondLife, a popular online virtual world, we found that recommenders relying on user similarity features obtained from the social network data clearly yielded the best results in terms of accuracy in case of predicting products, whereas the features obtained from the marketplace and location-based data sources also obtained very good results in case of predicting categories. This finding indicates that all three types of data sources are important and should be taken into account depending on the level of specialization of the recommendation task.