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CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Jester 2.0 (poster abstract): evaluation of an new linear time collaborative filtering algorithm
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A hybrid user model for news story classification
UM '99 Proceedings of the seventh international conference on User modeling
Finance with a personalized touch
Communications of the ACM
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Communications of the ACM
Growth in personalization and business
Communications of the ACM
Internet World Guide to One-to-One Web Marketing
Internet World Guide to One-to-One Web Marketing
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
International Journal of Electronic Commerce
A semantic-expansion approach to personalized knowledge recommendation
Decision Support Systems
Maintaining customer profiles in an e-commerce environment
Proceedings of the 2008 annual research conference of the South African Institute of Computer Scientists and Information Technologists on IT research in developing countries: riding the wave of technology
Personalized Advertising Strategy for Integrated Social Networking Websites
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Application of neural networks and Kano's method to content recommendation in web personalization
Expert Systems with Applications: An International Journal
An endorser discovering mechanism for social advertising
Proceedings of the 11th International Conference on Electronic Commerce
A diffusion mechanism for social advertising over microblogs
Decision Support Systems
Hi-index | 0.98 |
This paper describes a new personalized advertisement selection technique based on a customer's preference scores for product categories. This method performs well, despite having low data and analysis requirements, relative to other methods in use. Customer preference scores are updated based on a customer's initial profile, purchase history, and behavior in an Internet storefront, and are then used to select and display appropriate advertisements on Internet web pages when the customer visits the Internet storefront. Compared with currently available recommendation techniques such as collaborative filtering or rule-based methods, preference scoring techniques use only a single customer's data to select appropriate advertisements and do not require a learning data set, and yet have competitive performance and can reflect changes in a customers' preference. An experiment is performed to compare two alternative data storage structures, the preference table and the preference tree, with random selection and collaborative filtering.