GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Hipikat: recommending pertinent software development artifacts
Proceedings of the 25th International Conference on Software Engineering
Consistency-based diagnosis of configuration knowledge bases
Artificial Intelligence
Rascal: A Recommender Agent for Agile Reuse
Artificial Intelligence Review
Constraint-based recommender systems: technologies and research issues
Proceedings of the 10th international conference on Electronic commerce
Knowledge-Based Recommendation: Technologies and Experiences from Projects
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
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Constraint-based recommender applications provide valuable support in item selection processes related to complex products and services. This type of recommender operates on a knowledge base that contains a deep model of the product assortment as well as constraints representing the company's marketing and sales rules. Due to changes in the product assortment as well as in marketing and sales rules, such knowledge bases have to be adapted very quickly and frequently. In this paper we focus on a specific but very important aspect of recommender knowledge base development: we analyze the impact of different constraint representations on the cognitive effort of a knowledge engineer to successfully complete certain knowledge acquisition tasks. In this context, we report results of an initial empirical study and provide first basic recommendations regarding the design of recommender knowledge bases.