Mon, 21 Jul 2008 00:26:21 BST CiteULike: brusilovskys Radlinski http://www.citeulike.org/user/brusilovsky/author/Radlinski CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/brusilovsky/article/2682613 <i>(2007), pp. 417-418.</i> Recommending related papers based on digital library access records Stefan Pohl Filip Radlinski Thorsten Joachims doi:10.1145/1255175.1255260 (2007), pp. 417-418. 2008-04-17T15:53:00-00:00 2007 417 418 ACM academic-reference collaborative-filtering dlpaws log-mining http://www.citeulike.org/user/brusilovsky/article/2195687 <i>ACM Trans. Inf. Syst., Vol. 25, No. 2. (April 2007)</i><br /><br />This article examines the reliability of implicit feedback generated from clickthrough data and query reformulations in World Wide Web (WWW) search. Analyzing the users' decision process using eyetracking and comparing implicit feedback against manual relevance judgments, we conclude that clicks are informative but biased. While this makes the interpretation of clicks as absolute relevance judgments difficult, we show that relative preferences derived from clicks are reasonably accurate on average. We find that such relative preferences are accurate not only between results from an individual query, but across multiple sets of results within chains of query reformulations. Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search Thorsten Joachims Laura Granka Bing Pan Helene Hembrooke Filip Radlinski Geri Gay doi:10.1145/1229179.1229181 ACM Trans. Inf. Syst., Vol. 25, No. 2. (April 2007) 2008-01-04T21:39:32-00:00 2007 ACM Trans. Inf. Syst. 1046-8188 25 2 ACM en implicit-feedback information-retrieval www-search http://www.citeulike.org/user/brusilovsky/article/2237018 <i>(2005), pp. 239-248.</i><br /><br />In this paper, we present an alternative approach to the problem of contextual relevance feedback in web-based information retrieval. Our approach utilises a rich contextual model that exploits a user's implicit and explicit data. Each user's implicit data are gathered from their Internet search histories on their local machine. The user's explicit data are captured from a lexical database, a shared contextual knowledge base and domain-specific concepts using data mining techniques and a relevance feedback approach. This data is later used by our approach to modify queries to more accurately reflect the user's interests as well as to continually build the user's contextual profile and a shared contextual knowledge base. Finally, the approach retrieves personalised or contextual search results from the search engine using the modified/expanded query. Preliminary experiments indicate that our approach has the potential to not only aid in the contextual relevance feedback but also contribute towards the long term goal of intelligent relevance feedback in web-based information retrieval. Query chains: learning to rank from implicit feedback Filip Radlinski Thorsten Joachims doi:10.1145/1081870.1081899 (2005), pp. 239-248. 2008-01-16T02:10:45-00:00 2005 239 248 ACM en query-chain social-search www-search