Facet-Based User Modeling in Social Media for Personalized Ranking
Micro-blogging service has grown to a popular social media and provides a number of real-time messages for users. Although these messages allow users to access information on-the-fly, users often complain the problems of information overload and information shortage. Thus, a variety of methods of information filtering and recommendation are proposed, which are associated with user modeling. In this study, we propose an effective method of user modeling, facet-based user modeling, to capture user’s interests in social media. We evaluate our models in the context of personalized ranking of microblogs. Experiments on real-world data show that facet-based user modeling can provide significantly better ranking than traditional ranking methods. We also shed some light on how different facets impact user’s interest.
KeywordsSocial Medium User Modeling Mean Average Precision Normalize Discount Cumulative Gain Information Shortage
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