On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
Machine Learning
Communications of the ACM
Fab: content-based, collaborative recommendation
Communications of the ACM
Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
SoRec: social recommendation using probabilistic matrix factorization
Proceedings of the 17th ACM conference on Information and knowledge management
Matchbox: large scale online bayesian recommendations
Proceedings of the 18th international conference on World wide web
Learning to recommend with social trust ensemble
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Relation regularized matrix factorization
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Recommender systems with social regularization
Proceedings of the fourth ACM international conference on Web search and data mining
Like like alike: joint friendship and interest propagation in social networks
Proceedings of the 20th international conference on World wide web
Who should share what?: item-level social influence prediction for users and posts ranking
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
A multi-view content-based user recommendation scheme for following users in twitter
SocInfo'12 Proceedings of the 4th international conference on Social Informatics
Mining large streams of user data for personalized recommendations
ACM SIGKDD Explorations Newsletter
A general collaborative filtering framework based on matrix bordered block diagonal forms
Proceedings of the 24th ACM Conference on Hypertext and Social Media
Improve collaborative filtering through bordered block diagonal form matrices
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Mixing bandits: a recipe for improved cold-start recommendations in a social network
Proceedings of the 7th Workshop on Social Network Mining and Analysis
Proceedings of the first ACM conference on Online social networks
Non-negative multiple matrix factorization
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
A survey of collaborative filtering based social recommender systems
Computer Communications
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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.