Information retrieval: data structures and algorithms
Information retrieval: data structures and algorithms
Fab: content-based, collaborative recommendation
Communications of the ACM
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Expert-Driven Validation of Rule-Based User Models in Personalization Applications
Data Mining and Knowledge Discovery
The FindMe Approach to Assisted Browsing
IEEE Expert: Intelligent Systems and Their Applications
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Experiments in dynamic critiquing
Proceedings of the 10th international conference on Intelligent user interfaces
IEEE Transactions on Knowledge and Data Engineering
Comparing Recommendation Strategies in a Commercial Context
IEEE Intelligent Systems
An Integrated Environment for the Development of Knowledge-Based Recommender Applications
International Journal of Electronic Commerce
Case-studies on exploiting explicit customer requirements in recommender systems
User Modeling and User-Adapted Interaction
An efficient algorithm for automatic knowledge acquisition
Pattern Recognition
Collaborative filtering recommender systems
The adaptive web
Hybrid web recommender systems
The adaptive web
ISeller: A Flexible Personalization Infrastructure for e-Commerce Applications
EC-Web 2009 Proceedings of the 10th International Conference on E-Commerce and Web Technologies
Recommending Effort Estimation Methods for Software Project Management
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Recommendation systems with complex constraints: A course recommendation perspective
ACM Transactions on Information Systems (TOIS)
Harnessing geo-tagged resources for Web personalization
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Dimensions as Virtual Items: Improving the predictive ability of top-N recommender systems
Information Processing and Management: an International Journal
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Recommender Systems (RS) have become popular for their ability to make useful suggestions to online shoppers. Knowledge-based RS represent one branch of these types of applications that employ means-end knowledge to map abstract user requirements to product characteristics. Before setting up such a system, the knowledge has to be acquired from domain experts and formalized using constraints or a comparable representation mechanism. However, the initial acquisition of the knowledge base and its maintenance are effort intensive tasks. Here, we propose a system that learns rule-based preferences from successful interactions in historic transaction data. It is realized as a meta-level hybrid that employs collaborative filtering to derive preferences from a user's nearest neighbors that are processed by a knowledge-based RS to derive recommendations. An evaluation using a commercial dataset showed that this approach outperforms the prediction accuracy of a knowledge base provided by domain experts. In addition, the approach is applicable for supporting domain experts in the maintenance and validation tasks associated with providing personalization knowledge bases.