Exploiting social relations for query expansion and result ranking

Online communities have recently become a popular tool for publishing and searching content, as well as for finding and connecting to other users that share common interests. The content is typically user-generated and includes, for example, personal blogs, bookmarks, and digital photos. A particularly intriguing type of content is user-generated annotations (tags) for content items, as these concise string descriptions allow for reasonings about the interests of the user who created the content, but also about the user who generated the annotations. This paper presents a framework to cast the different entities of such networks into a unified graph model representing the mutual relationships of users, content, and tags. It derives scoring functions for each of the entities and relations. We have performed an experimental evaluation on two real-world datasets (crawled from deli.cio.us and Flickr) where manual user assessments of the query result quality show that our unified graph framework delivers high-quality results on social networks.