Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
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
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
A Taxonomy of Recommender Agents on theInternet
Artificial Intelligence Review
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
Data mining approach for analyzing call center performance
IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence
Mining conversational text for procedures with applications in contact centers
International Journal on Document Analysis and Recognition
Getting insights from the voices of customers: Conversation mining at a contact center
Information Sciences: an International Journal
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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Insurance riders are optional addendum to base insurance policies. In this paper we discuss the application of recommender systems to the task of matching riders to clients. This task is difficult because of the variety of possible riders, as well as the poor knowledge of the client over these riders. We focus on call centers where the agent also has limited knowledge and expertise. For such agents, discovering appropriate riders for the current client is very difficult, and automated tools that suggest such riders can play an important role in the agent-client dialogue, and may influence considerably the outcome of the interaction. This paper presents and discusses in detail the problem of recommending insurance riders to clients in call centers, comparing it to other, classic, recommendation system applications. In addition, we present an analysis of customer purchase behavior, showing that simple item-item recommendation algorithms provide good recommendations for riders given a base policy.