document: New objective functions for social collaborative filtering 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 aspects 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 custom-developed Facebook App involving data collected over five months from over 100 App users and their 37,000 + friends .
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