Thu, 07 Aug 2008 21:57:27 BST CiteULike: brusilovsky's datamining http://www.citeulike.org/user/brusilovsky/tag/datamining CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/brusilovsky/article/1853879 <i>(2007), pp. 300-309.</i> Time-dependent event hierarchy construction Gabriel Jeffrey Yu Huan Liu Philip Yu doi:10.1145/1281192.1281227 (2007), pp. 300-309. 2007-11-02T01:59:42-00:00 2007 300 309 ACM datamining news http://www.citeulike.org/user/brusilovsky/article/1826467 <i>(2006)</i><br /><br />Despite the success of global search engines, web site search engines are still suffering from poor performance. Since a web site is different from the whole web in link structure, access pattern, and data scale, it is not always successful when the methods which improve the performance of web search are applied to web site search. In this paper, we propose a novel algorithm to improve the retrieval performance by using web server logs. Web server logs are grouped into different sessions and the relationships of web pages in the session are analyzed based on their similarities. Then, a new web page representation is generated. Anchor text is used to create another representation. They are combined with original text-based representation in web site search. Two kinds of combination methods are investigated and tested: combination of document representations and combination of ranking scores. Our experimental results show that our algorithm can improve the retrieval accuracy for the four retrieval models we tested: Inference Network Model, Okapi Model, Cosine Similarity Model and TFIDF Model. The highest performance increase from web log analysis is from TFIDF model, and overall, inference network model with web log information achieves the best result. Improving web site search using web server logs Jin Zhou Chen Ding Dimitrios Androutsos doi:10.1145/1188966.1188996 (2006) 2007-10-26T19:16:18-00:00 2006 ACM datamining en log-mining social-search www-search http://www.citeulike.org/user/brusilovsky/article/1826402 <i>(2003), pp. 56-63.</i><br /><br />Current Web search engines generally impose link analysis-based re-ranking on web-page retrieval. However, the same techniques, when applied directly to small web search such as intranet and site search, cannot achieve the same performance because their link structures are different from the global Web. In this paper, we propose an approach to constructing implicit links by mining users' access patterns, and then apply a modified PageRank algorithm to re-rank web-pages for small web search. Our experimental results indicate that the proposed method outperforms content-based method by 16%, explicit link-based PageRank by 20% and DirectHit by 14%, respectively Implicit link analysis for small web search Gui-Rong Xue Hua-Jun Zeng Zheng Chen Wei-Ying Ma Hong-Jiang Zhang Chao-Jun Lu doi:10.1145/860435.860448 (2003), pp. 56-63. 2007-10-26T19:10:18-00:00 2003 56 63 ACM datamining en social-search www-search http://www.citeulike.org/user/brusilovsky/article/815479 <i>Information Sciences, Vol. In Press, Corrected Proof</i><br /><br />Search engines are among the most popular as well as useful services on the web. There is a need, however, to cater to the preferences of the users when supplying the search results to them. We propose to maintain the search profile of each user, on the basis of which the search results would be determined. This requires the integration of techniques for measuring search quality, learning from the user feedback and biased rank aggregation, etc. For the purpose of measuring web search quality, the "user satisfaction" is gauged by the sequence in which he picks up the results, the time he spends at those documents and whether or not he prints, saves, bookmarks, e-mails to someone or copies-and-pastes a portion of that document. For rank aggregation, we adopt and evaluate the classical fuzzy rank ordering techniques for web applications, and also propose a few novel techniques that outshine the existing techniques. A "user satisfaction" guided web search procedure is also put forward. Learning from the user feedback proceeds in such a way that there is an improvement in the ranking of the documents that are consistently preferred by the users. As an integration of our work, we propose a personalized web search system. Web search enhancement by mining user actions Sufyan Beg Nesar Ahmad doi:10.1016/j.ins.2006.06.011 Information Sciences, Vol. In Press, Corrected Proof 2006-08-24T14:04:05-00:00 Information Sciences In Press, Corrected Proof datamining www-search http://www.citeulike.org/user/brusilovsky/article/1267263 <i>Inf. Sci., Vol. 163, No. 1-3. (2004), pp. 135-156.</i> Mining massive document collections by the WEBSOM method Krista Lagus Samuel Kaski Teuvo Kohonen doi:10.1016/j.ins.2003.03.017 Inf. Sci., Vol. 163, No. 1-3. (2004), pp. 135-156. 2007-04-30T05:05:59-00:00 2004 Inf. Sci. 0020-0255 163 1-3 135 156 Elsevier Science Inc. datamining som