By Kalervo Järvelin (auth.), Paul Clough, Colum Foley, Cathal Gurrin, Gareth J. F. Jones, Wessel Kraaij, Hyowon Lee, Vanessa Mudoch (eds.)
This booklet constitutes the refereed court cases of the thirty third annual ecu convention on info Retrieval examine, ECIR 2011, held in Dublin, eire, in April 2010. The forty five revised complete papers offered including 24 poster papers, 17 brief papers, and six device demonstrations have been conscientiously reviewed and chosen from 223 complete examine paper submissions and sixty four poster/demo submissions. The papers are geared up in topical sections on textual content categorization, recommender platforms, internet IR, IR overview, IR for Social Networks, cross-language IR, IR idea, multimedia IR, IR purposes, interactive IR, and query answering /NLP.
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Extra info for Advances in Information Retrieval: 33rd European Conference on IR Research, ECIR 2011, Dublin, Ireland, April 18-21, 2011. Proceedings
One is that RBO may be very sensitive to the number of candidate categories, and therefore there should be further work in investigating ways to make the method invariant. The other one is that what needs to be one when the difference between local and global models is significantly large. These issues will be studied with more extensive experiments. Acknowledgement This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2010-0028079), and Global RFP program funded by Microsoft Research.
A possible reason is that diﬀerent 28 P. Li et al. Table 3. Results for clustering the documents under the nodes “Top/Computers/Programming/Languages”, “Top/Arts” and “Top”. indicates Folk-LDA achieves a signiﬁcant improvement over the LDA-M method. no. 248 subcategories of “Top” node have little in common and people seldom tag them with common tag terms. This dramatically reduces the factors that lead to topic drift in merged tag document. 4 Comparison of Clustering Based on Tags and/or Words Since user-related tag expansion greatly improves the performance of tag based web document clustering, it is natural to consider the following questions:(1)Are tags a better resource than words on document clustering?
3) Different prior distributions are assigned for original tag document and expanded tag document considering their diﬀerent generative processes, which ﬁts the data better compared to that using the same prior structure for both documents. Inference and Estimation. The main variables of interest are θd , θu , β t , which can be learned by maximizing the likelihood for the observation. Gibbs sampling  and variational inference  are two representative methods for solving LDA like models. Gibbs sampling has theoretical assurance that the ﬁnal results will converge to the true distribution while variational inference executes faster but with approximations.