GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
Learning to Optimally Schedule Internet Banner Advertisements
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
An MDP-Based Recommender System
The Journal of Machine Learning Research
Optimal pricing with recommender systems
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Conditioning Prices on Purchase History
Marketing Science
Explore/Exploit Schemes for Web Content Optimization
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
A contextual-bandit approach to personalized news article recommendation
Proceedings of the 19th international conference on World wide web
Nantonac collaborative filtering: a model-based approach
Proceedings of the fourth ACM conference on Recommender systems
ValuePick: Towards a Value-Oriented Dual-Goal Recommender System
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
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Many e-commerce sites use recommender systems, which suggest items that customers prefer. Though recommender systems have achieved great success, their potential is not yet fulfilled. One weakness of current systems is that the actions of the system toward customers are restricted to simply showing items. We propose a system that relaxes this restriction to offer price discounting as well as recommendations. The system can determine whether or not to offer price discounting for individual customers, and such a pricing scheme is called price personalization. We discuss how the introduction of price personalization improves the commercial viability of managing a recommender system, and thereby improving the customers' sense of the system's reliability. We then propose a method for adding price personalization to standard recommendation algorithms which utilize two types of customer data: preferential data and purchasing history. Based on the analysis of the experimental results, we reveal further issues in designing a personalized pricing recommender system.