New objective functions for social collaborative filtering

Joseph Noel, Scott Sanner, Khoi-Nguyen Tran, Peter Christen, Lexing Xie, Edwin V. Bonilla, Ehsan Abbasnejad, Nicolas Della Penna
2012 Proceedings of the 21st international conference on World Wide Web - WWW '12  
This paper examines the problem of social collaborative filtering (CF) to recommend items of interest to users in a social network setting. Unlike standard CF algorithms using relatively simple user and item features, recommendation in social networks poses the more complex problem of learning user preferences from a rich and complex set of user profile and interaction information. Many existing social CF methods have extended traditional CF matrix factorization, but have overlooked important
more » ... pects germane to the social setting. We propose a unified framework for social CF matrix factorization by introducing novel objective functions for training. Our new objective functions have three key features that address main drawbacks of existing approaches: (a) we fully exploit feature-based user similarity, (b) we permit direct learning of user-to-user information diffusion, and (c) we leverage co-preference (dis)agreement between two users to learn restricted areas of common interest. We evaluate these new social CF objectives, comparing them to each other and to a variety of (social) CF baselines, and analyze user behavior on live user trials in a customdeveloped Facebook App involving data collected over five months from over 100 App users and their 37,000+ friends.
doi:10.1145/2187836.2187952 dblp:conf/www/NoelSTCXBAP12 fatcat:2tmlwhnvsvffnofx7jggdmkhpa