Artificial Intelligence Review
Interactive Critiquing forCatalog Navigation in E-Commerce
Artificial Intelligence Review
IEEE Transactions on Knowledge and Data Engineering
Case-based recommender systems
The Knowledge Engineering Review
Acquiring and Revising Preferences in a Critique-Based Mobile Recommender System
IEEE Intelligent Systems
International Journal of Electronic Commerce
Replaying live-user interactions in the off-line evaluation of critique-based mobile recommendations
Proceedings of the 2007 ACM conference on Recommender systems
Long-term and session-specific user preferences in a mobile recommender system
Proceedings of the 13th international conference on Intelligent user interfaces
Preference-Based Organization Interfaces: Aiding User Critiques in Recommender Systems
UM '07 Proceedings of the 11th international conference on User Modeling
Mixed-Initiative Relaxation of Constraints in Critiquing Dialogues
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Product recommendation with interactive query management and twofold similarity
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Hybrid web recommender systems
The adaptive web
Case-based recommender systems: a unifying view
ITWP'03 Proceedings of the 2003 international conference on Intelligent Techniques for Web Personalization
Completeness criteria for retrieval in recommender systems
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
Combining case-based and similarity-based product recommendation
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
Case-based team recommendation
SocInfo'10 Proceedings of the Second international conference on Social informatics
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There are case-based recommender systems that generate personalized recommendations for users exploiting the knowledge contained in past recommendation cases. These systems assume that the quality of a new recommendation depends on the quality of the recorded recommendation cases. In this paper, we present a case model exploited in a mobile critique-based recommender system that generates recommendations using the knowledge contained in previous recommendation cases. The proposed case model is capable of modeling evolving (conversational) recommendation sessions, capturing the recommendation context, supporting critique-based user-system conversations, and integrating both ephemeral and stable user preferences. In this paper, we evaluate the proposed case model through replaying real recommendation cases recorded in a previous live-user evaluation. We measure the impact of the various components of the case model on the system's recommendation performance. The experimental results show that the case components that model the user's contextual information, default preferences, and initial preferences, are the most important for mobile context-dependent recommendation.