Tag-Based User Profiling for Social Media Recommendation

Contents

Making recommendations for social media presents special challenges. As tagging becomes common practice at many social media sites, this research proposes a new approach to user profiling based on the tags associated with one's personal collection of contents. To utilize the social interaction implied by tagging, a personal profile can be further extended with the tags specified by one's social contacts. A tag-to-tag matrix is defined to enable collaborative filtering-style recommendations without explicit user ratings. Experiments with collections of bookmarks and the associated tags from 42,463 users are presented and compared using the different views.