Social-Textual Search and Ranking

Web search engines are traditionally focused on textual content of data. Emergence of social networks and Web 2.0 applications makes it interesting to see how social data can be used in improving the conventional textual search on the web. In this paper, we focus on how to improve the effectiveness of web search by utilizing social data available from users, users actions and their underlying social network on the web. We define and formalize the problem of social-textual (socio-textual ) search and show how social aspect of the web can be effectively integrated into the textual search engines. We propose a new social relevance ranking based on several parameters including relationship between users, importance of each user and actions users perform on web documents (objects). We show how the proposed social ranking can be combined with the conventional textual relevance ranking. We have conducted an extensive set of experiments on the data from online radio website last.fm to evaluate the effectiveness of our proposed approaches. Our experimental results are very promising and show a significant improvement for socio-textual ranking over textual only and social only approaches.