Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
To buy or not to buy: mining airfare data to minimize ticket purchase price
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Content-based recommendation systems
The adaptive web
Recommender Systems: An Introduction
Recommender Systems: An Introduction
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Aggregating web offers to determine product prices
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Most e-commerce sites to-date have focused on helping consumers decide what to buy and where to buy. We study the complementary question of helping consumers decide when to buy, focusing on consumer durables. We introduce a utility-based model for evaluating different approaches to this question. We focus on how best to make use of forecasts in making recommendations, and propose three natural strategies. We establish a relationship between these strategies, and show that one of them is optimal. We conduct a large-scale experimental study to test the performance and robustness of these strategies. Across a wide range of conditions, the best strategy obtains 90% of the maximum possible gains.