Improving a recommender system through integration of user profiles: a semantic approach

The users are present in multiple social networks/virtual communities and each one can be considered as a source of information about this user. In face to this question it is important a mechanism to integrate the user profiles. Through the integration of user profiles it is possible identifier more accurately their interests analyzing other data sources that they are present, possible reducing the cold-start problem. In this context, we present a semantic approach to help integrate data from multiple sources, for the construction and maintenance of user profiles that will be used to improve the quality of a recommender system. To integrate data from multiple sources, we defined a heuristic that quantifies the importance of each data source for a given user. To validate our approach, we perform a case study, where the solution was coupled into a recommender system of papers focused in Software Engineering domain. The user profiles were built extracting their information from the Brazilian Curriculum Vitae database named CV-Lattes, an academic platform, and Linkedin, a business network. We compared the quality of the recommendation based on the profiles integrated and non-integrated. The results show the superior quality of the recommendation based on integrated profile.

In Social Recommender Systems Workshop at UMAP 2012