Collaborative filtering with privacy via factor analysis
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Scalable collaborative filtering using cluster-based smoothing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Fast maximum margin matrix factorization for collaborative prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo
Proceedings of the 25th international conference on Machine learning
Learning to recommend with social trust ensemble
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Learning personal + social latent factor model for social recommendation
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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With the rapidly growing amount of information available on the internet, recommender systems become popular tools to promote relevant online information to a given user. Although collaborative filtering is the most popular approach to build recommender systems and has been widely deployed in many applications, it still pay little attention to social actions, which are widely common in social networks and we believe could make a significant improvement in recommender systems. In this paper, we incorporate users' social actions into a model-based approach for recommendation using probabilistic matrix factorization. Compared with previous work, users' social actions are taken as a new relation to optimize previous trust-based recommender systems. To achieve this, we propose a social recommendation graphical model employing users' relations based on their social actions. We make use of users' commenting action in our approach and conduct experiments on a real life dataset, extracted from the Douban movie ratings and comments system. Our experiments demonstrate that incorporating users' social action information leads to a significant improvement in recommender systems.