Adaptive and multiple interest-aware user profiles for personalized search in folksonomy: A simple but effective graph-based profiling model

The data derived from the social tagging system, known as folksonomy, is a potentially useful source for understanding users' intentions. This study seeks to uncover some of the unexplored areas of folksonomy and examine the plausibility of new ideas for the improvement of personalized search. In particular, we challenge several state-of-the-art algorithms by exploiting folksonomy network structures used in creating user profiles that are adaptive and aware of multiple interests of a user, for the personalization of search results. The results obtained from the proposed approach shows a unanimous increase in the performance of personalization when compared to other state-of-the-art algorithms.