Item-based collaborative filtering recommendation algorithms
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IEEE Internet Computing
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ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
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On social networks and collaborative recommendation
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Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
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The YouTube video recommendation system
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Journal of the American Society for Information Science and Technology
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ICALP'05 Proceedings of the 32nd international conference on Automata, Languages and Programming
Auralist: introducing serendipity into music recommendation
Proceedings of the fifth ACM international conference on Web search and data mining
Using control theory for stable and efficient recommender systems
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Mining large streams of user data for personalized recommendations
ACM SIGKDD Explorations Newsletter
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Social media sites have used recommender systems to suggest items users might like but are not already familiar with. These items are typically movies, books, pictures, or songs. Here we consider an alternative class of items - pictures posted by design-conscious individuals. We do so in the context of a mobile application in which users find "cool" items in the real world, take pictures of them, and share those pictures online. In this context, temporal dynamics matter, and users would greatly profit from ways of identifying the latest design trends. We propose a new way of recommending trending pictures to users, which unfolds in three steps. First, two types of users are identified - those who are good at uploading trends (trend makers) and those who are experienced in discovering trends (trend spotters). Second, based on what those "special few" have uploaded and rated, trends are identified early on. Third, trends are recommended using existing algorithms. Upon the complete longitudinal dataset of the mobile application, we compare our approach's performance to a traditional recommender system's.