Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to recommend with social trust ensemble
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
A social recommendation framework based on multi-scale continuous conditional random fields
Proceedings of the 18th ACM conference on Information and knowledge management
Temporal recommendation on graphs via long- and short-term preference fusion
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Social networking feeds: recommending items of interest
Proceedings of the fourth ACM conference on Recommender systems
Recommender systems with social regularization
Proceedings of the fourth ACM international conference on Web search and data mining
We know what @you #tag: does the dual role affect hashtag adoption?
Proceedings of the 21st international conference on World Wide Web
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In recent years, online social networks have been dramatically expanded. Active users spend hours communicating with each other via these networks such that an enormous amount of data is created every second. The tremendous amount of newly created information costs users much time to discover interesting messages from their online social feeds. The problem is even exacerbated if users access these networks via mobile devices. To assist users in discovering interesting messages efficiently, in this paper, we propose a new approach to recommend interesting messages for each user by exploiting the user's response behavior. We extract data from the most popular social network, and the experimental results show that the proposed approach is effective and efficient.