Collaboration-based Social Tag Prediction in the Graph of Annotated Web Pages.

Different approaches based on content or tag information have been proposed to address the problem of tag recommendation for a web page. In this paper, we analyze two approaches in a graph of web pages. Each node is a web page and edges represent hyperlinks. The first approach uses the content while the second one uses tag information in the graph. The second approach makes two assumptions about the tag set of two interacting nodes. The Tag Similarity Assumption claims that two interacting nodes discuss about rather similar topics; therefore, the chance of having more similar tag set is higher. The Tag Collaboration Assumption says that two interacting nodes complement each others topics. We apply algorithms such as Self Organizing Map (SOM), Reinforcement Learning (RL) and K-means clustering to compare methods on several datasets. We conclude that tag-based tag predictors outperform their content-based peers by more than ten percent with respect to the cosine similarity between predicted and actual tag sets.