Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Evaluation of Item-Based Top-N Recommendation Algorithms
Proceedings of the tenth international conference on Information and knowledge management
Time weight collaborative filtering
Proceedings of the 14th ACM international conference on Information and knowledge management
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Recommender systems at the long tail
Proceedings of the fifth ACM conference on Recommender systems
Opportunity model for e-commerce recommendation: right product; right time
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Is it time for a career switch?
Proceedings of the 22nd international conference on World Wide Web
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In this paper, we design a recommender system for the post-purchase stage, i.e., after a user purchases a product. Our method combines both behavioral and content aspects of recommendations. We first find the most related categories for the active product in the post-purchase stage. Among these related categories, products with high behavioral relevance and content relevance are recommended to the user. In addition, our algorithm considers the temporal factor, i.e., the purchase time of the active product and the recommendation time. We apply our algorithm on a random sample of the purchase data from eBay. Comparing to the baseline item-based collaborative filtering approach, our hybrid recommender system achieves significant coverage and purchase rate gain for different time windows.