Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Collaborative filtering via gaussian probabilistic latent semantic analysis
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Time weight collaborative filtering
Proceedings of the 14th ACM international conference on Information and knowledge management
Personalized recommendation driven by information flow
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Recsplorer: recommendation algorithms based on precedence mining
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Serendipitous recommendations via innovators
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Social influence modeling on smartphone usage
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II
Discovering latent influence in online social activities via shared cascade poisson processes
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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This paper proposes a novel Collaborative Filtering scheme; it focuses on the dynamics and precedence of user preference to recommend items that match the latest preference of the target user. In predicting which items this user will purchase in the near future, the proposed algorithm identifies purchase history logs of users who have similar preferences and a high degree of purchase precedence (i.e., purchasing the same items earlier) relative to the target user. We call this metric the Personal Innovator Degree (PID). Experiments using real online sales data show that the proposed method outperforms existing methods.