BibSonomy publications for /user/mstrohm/searchThu Oct 15 14:58:41 CEST 2009WSDM (Late Breaking-Results)conf/wsdm/2009Tagging with Queries: How and Why?2009query-log-analysis search tagging taggingsurvey Thu Aug 27 02:03:21 CEST 2009Data Mining and Multi-agent Integration167--176Equipping Intelligent Agents with Commonsense Knowledge acquired from Search Query Logs: Results from an Exploratory Story 2009agents goals query-log-analysis search myown Access to knowledge about user goals represents a critical component for realizing the vision of intelligent agents acting upon user intent on the web. Yet, the manual acquisition of knowledge about user goals is costly and often infeasible. Ina departure from existing approaches, this paper proposes Goal Mining as a novel perspective for knowledge acquisition. Theresearch presented in this chapter makes the following contributions: (a) it presents Goal Mining as an emerging field of research and a corresponding automatic method for the acquisition of user goals from web corpora,in the case of this paper search query logs (b) it provides insights into the nature and some characteristics of these goalsand (c) it shows that the goals acquired from query logs exhibit traits of a long tail distribution, thereby providing accessto a broad range of user goals. Our results suggest that search query logs represent a viable, yet largely untapped resource for acquiring knowledge about explicit user goals.Wed Jul 01 09:26:27 CEST 2009New York, NY, USASIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval130--137SimFusion: measuring similarity using unified relationship matrix2005MUSTREAD folksonomy networks reading-group search tagging tools In this paper we use a Unified Relationship Matrix (URM) to represent a set of heterogeneous data objects (e.g., web pages, queries) and their interrelationships (e.g., hyperlinks, user click-through sequences). We claim that iterative computations over the URM can help overcome the data sparseness problem and detect latent relationships among heterogeneous data objects, thus, can improve the quality of information applications that require com- bination of information from heterogeneous sources. To support our claim, we present a unified similarity-calculating algorithm, SimFusion. By iteratively computing over the URM, SimFusion can effectively integrate relationships from heterogeneous sources when measuring the similarity of two data objects. Experiments based on a web search engine query log and a web page collection demonstrate that SimFusion can improve similarity measurement of web objects over both traditional content based algorithms and the cutting edge SimRank algorithm.Fri Apr 17 14:06:54 CEST 2009We Know'07 International Workshop on Collaborative Knowledge Management for Web Information Systems, in conjunction with WISE'07, Nancy, FranceHow Do Users Express Goals on the Web? - An Exploration of Intentional Structures in Web Search2007goals search web-science myown Wed Feb 11 11:18:39 CET 2009Digital Libraries: Universal and Ubiquitous Access to Information71--81Query Relaxation Based on Users’ Unconfidences on Query Terms and Web Knowledge Extraction2008MUSTREAD goals search This paper proposes a method to allow users to search for Web pages according to their search intentions. We introduce a degree of “unconfidence” for each term in a Web search query. We first investigate the relationships among query terms by accessinga Web search engine. Next, according to a degree of users’ unconfidences for each query term and the relationships among queryterms, our system finds alternative terms by accessing to a Web search engine. Then, our system generates a collection ofnew queries that are different from the original query and merges the Web search results obtained for each new query. We implementedour system and showed the usefulness of our system based on user evaluation.Thu Jan 29 20:50:26 CET 2009New York, NY, USACIKM '08: Proceeding of the 17th ACM conference on Information and knowledge management689--698Matching task profiles and user needs in personalized web search2008goals query-log-analysis reading-group search Thu Jan 29 20:49:28 CET 2009New York, NY, USACIKM '08: Proceeding of the 17th ACM conference on Information and knowledge management699--708Beyond the session timeout: automatic hierarchical segmentation of search topics in query logs2008goals query-log-analysis reading-group search Fri Jan 16 08:51:47 CET 2009Barcelona, SpainProceedings of the Workshop on Web Search Click Data WSCD'09, in conjunction with WSDM 2009Intentional Query Suggestion: Making user goals more explicit during search2009goals intentional query-log-analysis recommender search myown Thu Jan 08 13:50:44 CET 2009Proceedings of the 20th Belgian-Netherlands Conference on Artificial IntelligenceEffects of Goal-Oriented Search Suggestions2008goals recommender search Wed Nov 19 15:42:36 CET 2008Journal of the American Society for Information ScienceBates:794205--214Information Search tactics301979TOREAD cognition search strategies Thu Oct 30 22:18:21 CET 2008Proceedings of ACL/HLTImproving Search Results Quality by Customizing Summary Lengths2008MUSTREAD evaluation goals search Wed Oct 29 01:32:15 CET 20081The Social Life of Information2000TOBUY books cognition search social-factors social-search Fri Oct 10 13:41:15 CEST 200817th ACM Conference on Information and Knowledge Management Understanding the Relationship between Searchers' Queries and Information Goals2008goals search web-science We describe results from Web search log studies aimed at elucidating user behaviors associated with queries and destination URLs that appear with different frequencies. We note the diversity of information goals that searchers have and the differing ways that goals are specified. We examine rare and common information goals that are specified using rare or common queries. We identify several significant differences in user behavior depending on the rarity of the query and the destination URL. We find that searchers are more likely to be successful when the frequencies of the query and destination URL are similar. We also establish that the behavioral differences observed for queries and goals of varying rarity persist even after accounting for potential confounding variables, including query length, search engine ranking, session duration, and task difficulty. Finally, using an information-theoretic measure of search difficulty, we show that the benefits obtained by search and navigation actions depend on the frequency of the information goal.Sun Oct 05 20:44:18 CEST 2008International Workshop on Agents and Data Mining Interaction ADMI ' 08, in conjunction with WI '08Acquiring Explicit User Goals From Search Query Logs2008goals search social-factors social-search web-science myown Fri Oct 03 15:07:20 CEST 2008In Proc. of Computer-Supported Cooperative Work(CSCW)Towards a Model of Understanding Social Search2008search social-factors social-search web-science Sun Sep 21 16:17:37 CEST 2008SIGIRconf/sigir/2008163-170To personalize or not to personalize: modeling queries with variation in user intent2008SEMINAL goals query-log-analysis search Sun Sep 21 16:06:56 CEST 2008TorontoSIGIR conference on Research and development in information retrieval213--220Re-examining the potential effectiveness of interactive query expansion2003TOREAD search Much attention has been paid to the relative effectiveness of interactive query expansion versus automatic query expansion. Although interactive query expansion has the potential to be an effective means of improving a search, in this paper we show that, on average, human searchers are less likely than systems to make good expansion decisions. To enable good expansion decisions, searchers must have adequate instructions on how to use interactive query expansion functionalities. We show that simple instructions on using interactive query expansion do not necessarily help searchers make good expansion decisions and discuss difficulties found in making query expansion decisions.Wed Jul 23 13:12:34 CEST 2008Advances in Information Retrieval, 30th European Conference on IR Research, ECIR 2008101-113A Comparison of Social Bookmarking with Traditional Search49562008folksonomy search social-factors social-search taggingsurvey Social bookmarking systems allow users to store links to internet resources on a web page. As social bookmarking systems are growing in popularity, search algorithms have been developed that transfer the idea of link-based rankings in the Web to a social bookmarking system’s data structure. These rankings differ from traditional search engine rankings in that they incorporate the rating of users. In this study, we compare search in social bookmarking systems with traditionalWeb search. In the first part, we compare the user activity and behaviour in both kinds of systems, as well as the overlap of the underlying sets of URLs. In the second part,we compare graph-based and vector space rankings for social bookmarking systems with commercial search engine rankings. Our experiments are performed on data of the social bookmarking system Del.icio.us and on rankings and log data from Google, MSN, and AOL. We will show that part of the difference between the systems is due to different behaviour (e. g., the concatenation of multi-word lexems to single terms in Del.icio.us), and that real-world events may trigger similar behaviour in both kinds of systems. We will also show that a graph-based ranking approach on folksonomies yields results that are closer to the rankings of the commercial search engines than vector space retrieval, and that the correlation is high in particular for the domains that are well covered by the social bookmarking system.Wed Jul 23 12:52:57 CEST 2008New York, NY, USAJCDL '07: Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries107--116Can social bookmarking enhance search in the web?2007folksonomy search social-search taggingsurvey Tue Jul 22 13:44:52 CEST 2008New York, NY, USAWSDM '08: Proceedings of the international conference on Web search and web data mining195--206Can social bookmarking improve web search?2008folksonomy search taggingsurvey