2011/02/18 by Thomas Mandl, Mandl, Thomas, Christa Womser‐Hacker +2
Computer Science · #Advanced Database Systems and Queries #Data Management and Algorithms #Data Mining Algorithms and Applications #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Information Retrieval (cs.IR) #cs.HC #cs.IR
paper · pdf · doi:10.48550/arxiv.1102.3865
In: Ojala, Timo (ed.): Infotech Oulo International Workshop on Information Retrieval (IR 2001). Oulo, Finnland. 19.- 21.9.2001. S. 100-107
arxiv created 2011/02/18 · openalex publication_date 2011/02/18 · arxiv updated 2011/02/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Information Retrieval systems can be improved by exploiting context information such as user and document features. This article presents a model based on overlapping probabilistic or fuzzy clusters for such features. The model is applied within a fusion method which linearly combines several retrieval systems. The fusion is based on weights for the different retrieval systems which are learned by exploiting relevance feedback information. This calculation can be improved by maintaining a model for each document and user cluster. That way, the optimal retrieval system for each document or user type can be identified and applied. The extension presented in this article allows overlapping, probabilistic clusters of features to further refine the process.