Horting hatches an egg: a new graph-theoretic approach to collaborative filtering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy in e-commerce: examining user scenarios and privacy preferences
Proceedings of the 1st ACM conference on Electronic commerce
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
E-privacy in 2nd generation E-commerce: privacy preferences versus actual behavior
Proceedings of the 3rd ACM conference on Electronic Commerce
Mining e-commerce data: the good, the bad, and the ugly
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Personalization from incomplete data: what you don't know can hurt
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization
Data Mining and Knowledge Discovery
Innovative web use to learn about consumer behavior and online privacy
Communications of the ACM - Digital rights management
Use of a P3P user agent by early adopters
Proceedings of the 2002 ACM workshop on Privacy in the Electronic Society
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
HICSS '04 Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04) - Track 7 - Volume 7
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
A Framework for the Evaluation of Session Reconstruction Heuristics in Web-Usage Analysis
INFORMS Journal on Computing
On the Existence and Significance of Data Preprocessing Biases in Web-Usage Mining
INFORMS Journal on Computing
Personalised hypermedia presentation techniques for improving online customer relationships
The Knowledge Engineering Review
Impacts of user privacy preferences on personalized systems: a comparative study
Designing personalized user experiences in eCommerce
SIMT " A Privacy Preserving Web Metrics Tool
CEC '04 Proceedings of the IEEE International Conference on E-Commerce Technology
Privacy in e-commerce: stated preferences vs. actual behavior
Communications of the ACM - Transforming China
Learning user similarity and rating style for collaborative recommendation
ECIR'03 Proceedings of the 25th European conference on IR research
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Introduction to intelligent techniques for Web personalization
ACM Transactions on Internet Technology (TOIT)
From Web to Social Web: Discovering and Deploying User and Content Profiles
Towards Semantics-Based Instantiation of Services
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Intelligent techniques for web personalization
ITWP'03 Proceedings of the 2003 international conference on Intelligent Techniques for Web Personalization
Explaining the user experience of recommender systems
User Modeling and User-Adapted Interaction
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Numerous studies have demonstrated the effectiveness of personali-zation using quality criteria both from machine learning / data mining and from user studies. However, a site requires more than a high-performance personalization algorithm: it needs to convince its users to input the data needed by the algorithm. Today's Web users are becoming increasingly privacy-conscious and less willing to disclose personal data. How can the advantages of personalization (and hence, of disclosure) be communicated effectively, and how can the success of such strategies be measured in terms of improved personalization quality? In this paper, we argue for a tighter integration of the HCI and computational issues involved in these questions. We first outline the problems for personalization that arise from the combination of users' privacy concerns and sites' current policies of dealing with privacy issues. We then describe the results of an experiment that investigated the effects of changes to a site's interface on users' willingness to disclose data for personalization. This is followed by an overview of studies of the sensitivity of mining algorithms to changes in the availability of these types of data. Based on this, we outline a research agenda for future evaluation studies and user agent design.