Communications of the ACM
The Effects of Training Set Size on Decision Tree Complexity
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Data completeness: a key to effective net-based customer service systems
Communications of the ACM - E-services: a cornucopia of digital offerings ushers in the next Net-based evolution
Web-Log Mining for Predictive Web Caching
IEEE Transactions on Knowledge and Data Engineering
Applying data mining technology to analyze user behavior in course website
ACST'07 Proceedings of the third conference on IASTED International Conference: Advances in Computer Science and Technology
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
Expert Systems with Applications: An International Journal
Optimizing web structures using web mining techniques
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Expert Systems with Applications: An International Journal
Preprocessing the web server logs: an illustrative approach for effective usage mining
ACM SIGSOFT Software Engineering Notes
Feature evaluation for web crawler detection with data mining techniques
Expert Systems with Applications: An International Journal
A literature review and classification of recommender systems research
Expert Systems with Applications: An International Journal
Web data mining trends and techniques
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
A comparison of web robot and human requests
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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Retention recommendation has been an important topic in e-commerce. Subjective classification is a potentially useful approach for both better understanding customer Web logs and identifying information actionable to customer retention. Subjective classification seems attractive because obtaining a large set of objective data, with labeling for training and testing, is often difficult. In particular, building a classifier when a training data set is small and possibly inaccurate is important. That's because decision makers find that identifying user purchase patterns from a Web log is difficult-there's no direct relationship between Web log data and purchase patterns. It's also difficult because the information in the small training data set is insufficient. A proposed method to build a classifier further selects a small subset of the training data set to build a classifier that possibly leads to high accuracy. This approach can help identify whether customers have purchase interest. The result of such classification provides actionable patterns and helps companies gain high customer retention.