Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Time spent on a web page is sufficient to infer a user's interest
IMSA'07 IASTED European Conference on Proceedings of the IASTED European Conference: internet and multimedia systems and applications
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Beyond the session timeout: automatic hierarchical segmentation of search topics in query logs
Proceedings of the 17th ACM conference on Information and knowledge management
Personalized Delivery of On---Line Search Advertisement Based on User Interests
APWeb/WAIM '09 Proceedings of the Joint International Conferences on Advances in Data and Web Management
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
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Whether or not the C2C customers would click the advertisement heavily relies on advertisement content relevance and customers' searching progress. For example, when starting a purchasing task, customers are more likely to click the advertisements of their target products; while approaching the end, advertisements on accessories of the target products may interest the customers more. Therefore, the understanding of search progress on target products is very important in improving adaptive advertisement strategies. Search progress can be estimated by the time spent on the target product and the total time will be spent on this product. For the purpose of providing important information for product progress estimation, we propose a product duration prediction problem. Due to the similarities between the product duration prediction problem and user preference prediction problem (e.g. Large number of users, a history of past behaviors and ratings), the present work relies on the collaborative filtering method to estimate the searching duration of performing a purchasing task. Comparing neighbor-based, singular vector decomposition(SVD) and biased SVD method, we find biased SVD is superior to the others.