Web search engines are traditionally focused on textual con- tent 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 ef- fectiveness of web search by utilizing social data available from users, users actions and their underlying social net- work on the web. We de ne and formalize the problem of social-textual (socio-textual ) search and show how social as- pect of the web can be e ectively 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 so- cial 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 e ectiveness of our proposed approaches. Our experimental results are very promising and show a signi - cant improvement for socio-textual ranking over textual only and social only approaches.
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