http://www.bibsonomy.org/burst/user/minguezBibSonomy BuRST Feed for /user/minguez2008-07-26T21:33:02+02:00Dissertationhttp://www.bibsonomy.org/bibtex/2880e27782ec86737a931b9d92d9e905a/minguezminguez2008-04-03T13:26:44+02:00recommender matchmaking master <span style="color:#555555;">G. <a href="http://www.bibsonomy.org/author/Linden">Linden</a> and B. <a href="http://www.bibsonomy.org/author/Smith">Smith</a> and J. <a href="http://www.bibsonomy.org/author/York">York</a> </span><em>Internet Computing, IEEE</em><em>7(1):76--80</em>(<em>2003</em>)Thu Apr 03 13:26:44 CEST 2008Internet Computing, IEEE176--80Amazon.com recommendations: item-to-item collaborative filtering72003recommender matchmaking master Recommendation algorithms are best known for their use on e-commerce Web sites, where they use input about a customer's interests to generate a list of recommended items. Many applications use only the items that customers purchase and explicitly rate to represent their interests, but they can also use other attributes, including items viewed, demographic data, subject interests, and favorite artists. At Amazon.com, we use recommendation algorithms to personalize the online store for each customer. The store radically changes based on customer interests, showing programming titles to a software engineer and baby toys to a new mother. There are three common approaches to solving the recommendation problem: traditional collaborative filtering, cluster models, and search-based methods. Here, we compare these methods with our algorithm, which we call item-to-item collaborative filtering. Unlike traditional collaborative filtering, our algorithm's online computation scales independently of the number of customers and number of items in the product catalog. Our algorithm produces recommendations in real-time, scales to massive data sets, and generates high quality recommendations.Analysis of recommendation algorithms for e-commercehttp://www.bibsonomy.org/bibtex/28761b337bd08a4ac7b72bac4c84851fb/minguezminguez2008-04-03T13:20:29+02:00master recommender matchmaking <span style="color:#555555;">Badrul <a href="http://www.bibsonomy.org/author/Sarwar">Sarwar</a> and George <a href="http://www.bibsonomy.org/author/Karypis">Karypis</a> and Joseph <a href="http://www.bibsonomy.org/author/Konstan">Konstan</a> and John <a href="http://www.bibsonomy.org/author/Riedl">Riedl</a> </span><em>EC '00: Proceedings of the 2nd ACM conference on Electronic commerce, </em><em>page158--167. </em><em>New York, NY, USA, </em><em>ACM Press, </em>(<em>2000</em>)Thu Apr 03 13:20:29 CEST 2008New York, NY, USAEC '00: Proceedings of the 2nd ACM conference on Electronic commerce158--167Analysis of recommendation algorithms for e-commerce2000master recommender matchmaking dblphttp://www.bibsonomy.org/bibtex/27b18c5dec3672081adfdcfc000e3ff50/minguezminguez2008-03-25T16:21:10+01:00descriptionlogic master matchmaking <span style="color:#555555;">Matthias <a href="http://www.bibsonomy.org/author/Klusch">Klusch</a> and Benedikt <a href="http://www.bibsonomy.org/author/Fries">Fries</a> and Katia <a href="http://www.bibsonomy.org/author/Sycara">Sycara</a> </span><em>AAMAS, </em><em>page915-922. </em><em>ACM, </em>(<em>2006</em>)Tue Mar 25 16:21:10 CET 2008AAMASconf/atal/2006915-922Automated semantic web service discovery with OWLS-MX.2006descriptionlogic master matchmaking http://www.bibsonomy.org/bibtex/21bfb5f33156769d97d22bf8d5b4369dc/minguezminguez2008-03-25T16:04:37+01:00matchmaking descriptionlogic master <span style="color:#555555;">Alexander <a href="http://www.bibsonomy.org/author/Borgida">Borgida</a> and Thomas <a href="http://www.bibsonomy.org/author/Walsh">Walsh</a> and Haym <a href="http://www.bibsonomy.org/author/Hirsh">Hirsh</a> </span><em>Proceedings of the 2005 International Workshop on Description Logics (DL2005), July 26-28, 2005, Edinburgh, Scotland, UK, </em><em>volume147ofCEUR Workshop Proceedings, </em><em>CEUR-WS.org, </em>(<em>2005</em>)Tue Mar 25 16:04:37 CET 2008Proceedings of the 2005 International Workshop on Description Logics (DL2005), July 26-28, 2005, Edinburgh, Scotland, UKCEUR Workshop ProceedingsTowards Measuring Similarity in Description Logics.1472005matchmaking descriptionlogic master http://www.bibsonomy.org/bibtex/25af761051f5ee7543ac1606fbd91ea96/minguezminguez2008-03-25T16:01:13+01:00matchmaking sparql master <span style="color:#555555;">Christoph <a href="http://www.bibsonomy.org/author/Kiefer">Kiefer</a> and Abraham <a href="http://www.bibsonomy.org/author/Bernstein">Bernstein</a> and Markus <a href="http://www.bibsonomy.org/author/Stocker">Stocker</a> </span><em>Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea, </em><em>volume4825ofLNCS, </em><em>page295--308. </em><em>Berlin, Heidelberg, </em><em>Springer Verlag, </em><em>November2007. </em>Tue Mar 25 16:01:13 CET 2008Berlin, HeidelbergProceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Koreahttp://data.semanticweb.org/conference/iswc-aswc/2007/proceedingsNovember295--308LNCSThe Fundamentals of iSPARQL - A Virtual Triple Approach For Similarity-Based Semantic Web Tasks48252007matchmaking sparql master This research explores three SPARQL-based techniques to solve Semantic Web tasks that often require similarity measures, such as semantic data integration, ontology mapping, and Semantic Web service matchmaking. Our aim is to see how far it is possible to integrate customized similarity functions (CSF) into SPARQL to achieve good results for these tasks. Our first approach exploits virtual triples calling property functions to establish virtual relations among resources under comparison; the second approach uses extension functions to filter out resources that do not meet the requested similarity criteria; finally, our third technique applies new solution modifiers to post-process a SPARQL solution sequence. The semantics of the three approaches are formally elaborated and discussed. We close the paper with a demonstration of the usefulness of our iSPARQL framework in the context of a data integration and an ontology mapping experiment.dblphttp://www.bibsonomy.org/bibtex/20e3fd7b6cceacfea45f1166669bd33a1/minguezminguez2008-03-25T16:00:00+01:00similarity matchmaking master <span style="color:#555555;">Dekang <a href="http://www.bibsonomy.org/author/Lin">Lin</a> </span><em>Proceedings of the Fifteenth International Conference on Machine Learning (ICML 1998), Madison, Wisconson, USA, July 24-27, 1998, </em><em>page296-304. </em><em>Morgan Kaufmann, </em>(<em>1998</em>)Tue Mar 25 16:00:00 CET 2008Proceedings of the Fifteenth International Conference on Machine Learning (ICML 1998), Madison, Wisconson, USA, July 24-27, 1998296-304An Information-Theoretic Definition of Similarity1998similarity matchmaking master dblphttp://www.bibsonomy.org/bibtex/275183eed0a84f9c9e0b0a170a23ec54a/minguezminguez2008-03-25T15:56:54+01:00matchmaking similarity master <span style="color:#555555;">Jay J. <a href="http://www.bibsonomy.org/author/Jiang">Jiang</a> and David W. <a href="http://www.bibsonomy.org/author/Conrath">Conrath</a> </span><em>CoRR</em>(<em>1997</em>) <em>informal publication . </em>Tue Mar 25 15:56:54 CET 2008CoRRinformal publicationSemantic Similarity Based on Corpus Statistics and Lexical Taxonomycmp-lg/97090081997matchmaking similarity master http://www.bibsonomy.org/bibtex/2454781d9c6deadeae45d0eba0d0cdf91/minguezminguez2008-03-25T15:53:18+01:00similarity master matchmaking <span style="color:#555555;">Philip <a href="http://www.bibsonomy.org/author/Resnik">Resnik</a> </span>(<em>1995</em>)Tue Mar 25 15:53:18 CET 2008Using Information Content to Evaluate Semantic Similarity in a Taxonomy1995similarity master matchmaking This paper presents a new measure of semantic similarity in an IS-A taxonomy, based on the notion of information content. Experimental evaluation suggests that the measure performs encouragingly well (a correlation of r = 0.79 with a benchmark set of human similarity judgments, with an upper bound of r = 0.90 for human subjects performing the same task), and significantly better than the traditional edge counting approach (r = 0.66).Using Corpus Statistics and WordNet Relations for Sense Identification - Leacock, Chodorow, Miller (ResearchIndex)http://www.bibsonomy.org/bibtex/2a16e81a95c661887d3e6e14483efe693/minguezminguez2008-03-25T15:28:54+01:00master matchmaking similarity <span style="color:#555555;">Claudia <a href="http://www.bibsonomy.org/author/Leacock">Leacock</a> and Martin <a href="http://www.bibsonomy.org/author/Chodorow">Chodorow</a> and George A. <a href="http://www.bibsonomy.org/author/Miller">Miller</a> </span><em>Computational Linguistics</em><em>24(1):147-165</em>(<em>1998</em>)Tue Mar 25 15:28:54 CET 2008Computational Linguistics1147-165Using Corpus Statistics and WordNet Relations for Sense Identification241998master matchmaking similarity dblphttp://www.bibsonomy.org/bibtex/2b71e50d2998528d120b95763104ccf07/minguezminguez2008-03-25T14:07:36+01:00similarity matchmaking master <span style="color:#555555;">Zhibiao <a href="http://www.bibsonomy.org/author/Wu">Wu</a> and Martha Stone <a href="http://www.bibsonomy.org/author/Palmer">Palmer</a> </span><em>Proceedings of the 32nd. Annual Meeting of the Association for Computational Linguistics (ACL 1994), </em><em>page133-138. </em>(<em>1994</em>)Tue Mar 25 14:07:36 CET 2008Proceedings of the 32nd. Annual Meeting of the Association for Computational Linguistics (ACL 1994)133-138Verb Semantics and Lexical Selection1994similarity matchmaking master http://www.bibsonomy.org/bibtex/27489a4e2594e6ec10048b740bcccb34c/minguezminguez2008-03-25T13:58:49+01:00master matchmaking similarity <span style="color:#555555;">R. <a href="http://www.bibsonomy.org/author/Rada">Rada</a> and H. <a href="http://www.bibsonomy.org/author/Mili">Mili</a> and E. <a href="http://www.bibsonomy.org/author/Bicknell">Bicknell</a> and M. <a href="http://www.bibsonomy.org/author/Blettner">Blettner</a> </span>(<em>1989</em>)Tue Mar 25 13:58:49 CET 2008IEEE Transactions on Systems, Man and Cybernetics17-30Development and application of a metric on semantic nets191989master matchmaking similarity Motivated by the properties of spreading activation and conceptual distance, the authors propose a metric, called distance, on the power set of nodes in a semantic net. Distance is the average minimum path length over all pairwise combinations of nodes between two subsets of nodes. Distance can be successfully used to assess the conceptual distance between sets of concepts when used on a semantic net of hierarchical relations. When other kinds of relationships, like `cause', are used, distance must be amended but then can again be effective. The judgements of distance significantly correlate with the distance judgements that people make and help to determine whether one semantic net is better or worse than another. The authors focus on the mathematical characteristics of distance that presents novel cases and interpretations. Experiments in which distance is applied to pairs of concepts and to sets of concepts in a hierarchical knowledge base show the power of hierarchical relations in representing information about the conceptual distance between conceptshttp://www.bibsonomy.org/bibtex/2340187a0fa89b82d55e8dfaf049160f4/minguezminguez2008-03-25T13:25:46+01:00matchmaking fuzzyLogic master <span style="color:#555555;">Sudhir <a href="http://www.bibsonomy.org/author/Agarwal">Agarwal</a> and Steffen <a href="http://www.bibsonomy.org/author/Lamparter">Lamparter</a> </span><em>Proceedings of the 7th International IEEE Conference on E-Commerce Technology 2005, </em><em>Munich, Germany, </em><em>IEEE Computer Society, </em><em>JUL2005. </em>Tue Mar 25 13:25:46 CET 2008Munich, GermanyProceedings of the 7th International IEEE Conference on E-Commerce Technology 2005JUL{sMart - A Semantic Matchmaking Portal for Electronic Markets}2005matchmaking fuzzyLogic master