By Kalervo Järvelin (auth.), Paul Clough, Colum Foley, Cathal Gurrin, Gareth J. F. Jones, Wessel Kraaij, Hyowon Lee, Vanessa Mudoch (eds.)
This e-book constitutes the refereed lawsuits of the thirty third annual ecu convention on info Retrieval learn, ECIR 2011, held in Dublin, eire, in April 2010. The forty five revised complete papers provided including 24 poster papers, 17 brief papers, and six instrument demonstrations have been rigorously reviewed and chosen from 223 complete study paper submissions and sixty four poster/demo submissions. The papers are prepared in topical sections on textual content categorization, recommender structures, net IR, IR assessment, IR for Social Networks, cross-language IR, IR thought, multimedia IR, IR functions, interactive IR, and query answering /NLP.
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Additional info for Advances in Information Retrieval: 33rd European Conference on IR Research, ECIR 2011, Dublin, Ireland, April 18-21, 2011. Proceedings
The widespread tag sparsity seriously aﬀects the accurate estimation of document similarity and limits the use of tags in most cases. To address the problem, we present a novel approach in this research by incorporating additional related tags. Speciﬁcally, we create a virtual expanded tag document for each tagging user of each web page. The user-related expanded tag document consists of tags in the user’s bookmarks which are required to contain tags of associated web pages. The detailed construction process is illustrated in Figure 1.
The higher the relevance score of a category is in comparison with others, the higher weight it is given. Note that relevance scores are calculated with no regard to the hierarchy information since all the documents under a categoryis treated as a mega document regardless of its position in the hierarchy. In this work, we use cosine similarity between the input document to be classified and the set of documents belonging to the candidate category at hand. Based on this interpretation, a mixture weight is calculated as follows: local λi = RelScore( ci ∑ RelScore(c ) local k ) (10) k where k is the number of candidate categories for the initial search result.
The shrinkage method requires heavy computation not only because of the need to consider all the data on the path to the root but also because of the time for parameter estimations with the EM algorithm. By focusing on the top- and leaf-level information, our method reduces time complexity considerably. A recent study proposed the narrow-down approach. The algorithm consists of search and classification stages. In classification, the algorithm just focused on local information by utilizing trigram language model classifier.