Towards Comprehensive User Modeling on the Social Web for Personalized Link Recommendations

Abstract

User modeling for individual users on the Social Web plays a significant role and is a fundamental step for personalization as well as recommendations. Previous studies have proposed various user modeling strategies in different dimensions such as (1) interest representation, (2) interest propagation, (3) content enrichment and (4) temporal dynamics of user interests. This research mainly focuses on the first two dimensions interest representation and propagation. In addition, we also investigate the combination of these four dimensions and their synergistic effect on the quality of user modeling. Different user modeling strategies will then be evaluated in the context of personalized link recommender systems using standard evaluation methodologies such as Mean Reciprocal Rank (MRR), recall (R@N) and success (S@N) at rank N.