Combination of evidence in recommendation systems characterized by distance functions

Recommendation systems for different document networks (DN), such as the World Wide Web, digital libraries, or scientific databases, often make use of distance functions extracted from relationships among documents and between documents and semantic tags. The distance functions computed from these relations establish associative networks among items of the DN, and allow recommendation systems to identify relevant associations for individual users. The process of recommendation can be improved by integrating associative data from different sources. Thus, we are presented with a problem of combining evidence (about associations between items) from different sources characterized by distance functions. In this paper we summarize our work on: (1) inferring associations from semi-metric distance functions; and (2) combining evidence from different (distance) associative DN.