An outranking approach for rank aggregation in information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Multi-objective optimization in learning to rank
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Interaction and personalization of criteria in recommender systems
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
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Information retrieval models usually represent content only, and not other considerations, such as authority, cost, and recency. How could multiple criteria be utilized in information retrieval, and how would it effect the results? In our experiments, using multiple user-centric criteria always produced better results than a single criteria.