Personalizing and Improving Resource Recommendation by Analyzing Users Preferences in Social Tagging Activities.

Collaborative tagging which is the keystone of the social practices of web 2.0 has been highly developed in the last few years. In this paper, we propose a new method to analyze user profiles according to their tagging activity in order to improve resource recommendation. We base upon association rules which is a powerful method to discover interesting relationships among large datasets on the web. Focusing on association rules we can find correlations between tags in a social network. Our aim is to recommend resources annotated with tags suggested by association rules, in order to enrich user profiles. The effectiveness of the recommendation depends on the resolution of social tagging drawbacks. In our recommender process, we demonstrate how we can reduce tag ambiguity and spelling variations problems by taking into account social similarities calculated on folksonomies, in order to personalize resource recommendation. We surmount also the lack of semantic links between tags during the recommendation process. Experiments are carried out with two different scenarios: the first one is a proof of concept over two baseline datasets and the second one is a real world application for diabetes disease.