Search (115 results, page 1 of 6)

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Abstract

Involving users in early phases of software development has become a common strategy as it enables developers to consider user needs from the beginning. Once a system is in production, new opportunities to observe, evaluate and learn from users emerge as more information becomes available. Gathering information from users to continuously evaluate their behavior is a common practice for commercial software, while the Cranfield paradigm remains the preferred option for Information Retrieval (IR) and recommendation systems in the academic world. Here we introduce the Infrastructures for Living Labs STELLA project which aims to create an evaluation infrastructure allowing experimental systems to run along production web-based academic search systems with real users. STELLA combines user interactions and log files analyses to enable large-scale A/B experiments for academic search.

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Abstract

A non-negligible fraction of user queries end up with very few or even no matching results in leading commercial web search engines. In this work, we provide a detailed characterization of such queries and show that search engines try to improve such queries by showing the results of related queries. Through a user study, we show that these query suggestions are usually perceived as relevant. Also, through a query log analysis, we show that the users are dissatisfied after submitting a query that match no results at least 88.5% of the time. As a first step towards solving these no-answer queries, we devised a large number of features that can be used to identify such queries and built machine-learning models. These models can be useful for scenarios such as the mobile- or meta-search, where identifying a query that will retrieve no results at the client device (i.e., even before submitting it to the search engine) may yield gains in terms of the bandwidth usage, power consumption, and/or monetary costs. Experiments over query logs indicate that, despite the heavy skew in class sizes, our models achieve good prediction quality, with accuracy (in terms of area under the curve) up to 0.95.

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Date

29. 3.1996 18:16:49

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Abstract

This paper examines search failures in document retrieval systems. Since search failures are closely related to overall document retrieval system performance, the paper briefly discusses retrieval effectiveness measures such as precision and recall. It examines 4 methods used to study retrieval failures: retrieval effectiveness measures, user satisfaction measures, transaction log analysis, and the critical incident technique. It summarizes the findings of major failure anaylsis studies and identifies the types of failures that usually occur in document retrieval systems

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Abstract

Die deutschen Suchmaschinen, Abacho, Acoon, Fireball und Lycos sowie die Web-Kataloge Web.de und Yahoo! werden einem Qualitätstest nach relativem Recall, Precision und Availability unterzogen. Die Methoden der Retrievaltests werden vorgestellt. Im Durchschnitt werden bei einem Cut-Off-Wert von 25 ein Recall von rund 22%, eine Precision von knapp 19% und eine Verfügbarkeit von 24% erreicht

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Abstract

The objective of this research is to discover the search state patterns through which users retrieve information in hypertext systems. The Markov model is used to describe users' search behavior. As determined by the log-linear model test, the second-order Markov model is the best model. Search patterns of different user groups were studied by comparing the corresponding transition probability matrices. The comparisons were made based on the following factors: gender, search experience, search task, and the user's academic background. The statistical tests revealed that there were significant differences between all the groups being compared

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Abstract

Examines the searching behaviour of Guardian journalists searching FT PROFILE online system. Using transactional log analysis compares the searching styles of journalists with those of Guardian librarians. In some respects end users conform to the picture that professionals have of them - they search with a very limited range of commands - but in other respects they confound that image - they are very quick and economical searchers. Their behaviour relates to their general information seeking behaviour, and their searching styles would be seen in this regard

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Abstract

Latent Semantic Indexing (LSI), when applied to semantic space built an text collections, improves information retrieval, information filtering, and word sense disambiguation. A new dual probability model based an the similarity concepts is introduced to provide deeper understanding of LSI. Semantic associations can be quantitatively characterized by their statistical significance, the likelihood. Semantic dimensions containing redundant and noisy information can be separated out and should be ignored because their negative contribution to the overall statistical significance. LSI is the optimal solution of the model. The peak in the likelihood curve indicates the existence of an intrinsic semantic dimension. The importance of LSI dimensions follows the Zipf-distribution, indicating that LSI dimensions represent latent concepts. Document frequency of words follows the Zipf distribution, and the number of distinct words follows log-normal distribution. Experiments an five standard document collections confirm and illustrate the analysis.

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Source

Online. 22(1998) no.3, S.24-26,28

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Abstract

Proceedings of the llth TREC-conference held in Gaithersburg, Maryland (USA), November 19-22, 2002. Aim of the conference was discussion an retrieval and related information-seeking tasks for large test collection. 93 research groups used different techniques, for information retrieval from the same large database. This procedure makes it possible to compare the results. The tasks are: Cross-language searching, filtering, interactive searching, searching for novelty, question answering, searching for video shots, and Web searching.

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Abstract

This article addresses the question of whether the Web can serve as an information source for research. Specifically, it analyzes by way of content analysis the Web pages retrieved by the major search engines on a particular date (June 7, 1998), as a result of the query 'informetrics OR informetric'. In 807 out of the 942 retrieved pages, the search terms were mentioned in the context of information science. Over 70% of the pages contained only indirect information on the topic, in the form of hypertext links and bibliographical references without annotation. The bibliographical references extracted from the Web pages were analyzed, and lists of most productive authors, most cited authors, works, and sources were compiled. The list of reference obtained from the Web was also compared to data retrieved from commercial databases. For most cases, the list of references extracted from the Web outperformed the commercial, bibliographic databases. The results of these comparisons indicate that valuable, freely available data is hidden in the Web waiting to be extracted from the millions of Web pages

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Abstract

Reports a comparison of the retrieval effectiveness of the AltaVista, Excite and Lycos Web search engines. Describes a method for comparing the recall of the 3 sets of searches, despite the fact that they are carried out on non identical sets of Web pages. It is thus possible, unlike previous comparative studies of Web search engines, to consider both recall and precision when evaluating the effectiveness of search engines

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Abstract

Purpose - The aim of the paper is to put forward a structured mechanism for web search evaluation. The paper seeks to point to useful scientific research and show how information practitioners can use these methods in evaluation of search on the web for their users. Design/methodology/approach - The paper puts forward an approach which utilizes traditional laboratory-based evaluation measures such as average precision/precision at N documents, augmented with diagnostic measures such as link broken, etc., which are used to show why precision measures are depressed as well as the quality of the search engines crawling mechanism. Findings - The paper shows how to use diagnostic measures in conjunction with precision in order to evaluate web search. Practical implications - The methodology presented in this paper will be useful to any information professional who regularly uses web search as part of their information seeking and needs to evaluate web search services. Originality/value - The paper argues that the use of diagnostic measures is essential in web search, as precision measures on their own do not allow a searcher to understand why search results differ between search engines.

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Abstract

Purpose The purpose of this paper is to propose a method to have more accurate results in comparing performance of the paired information retrieval (IR) systems with reference to the current method, which is based on the mean effectiveness scores of the systems across a set of identified topics/queries. Design/methodology/approach Based on the proposed approach, instead of the classic method of using a set of topic scores, the documents level scores are considered as the evaluation unit. These document scores are the defined document's weight, which play the role of the mean average precision (MAP) score of the systems as a significance test's statics. The experiments were conducted using the TREC 9 Web track collection. Findings The p-values generated through the two types of significance tests, namely the Student's t-test and Mann-Whitney show that by using the document level scores as an evaluation unit, the difference between IR systems is more significant compared with utilizing topic scores. Originality/value Utilizing a suitable test collection is a primary prerequisite for IR systems comparative evaluation. However, in addition to reusable test collections, having an accurate statistical testing is a necessity for these evaluations. The findings of this study will assist IR researchers to evaluate their retrieval systems and algorithms more accurately.

Date

20. 1.2015 18:30:22

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Abstract

Searching for information on the WWW basically comes down to locating an appropriate Web site and to retrieving relevant information from that site. This study examined the effect of a user's WWW experience on both phases of the search process. 35 students from 2 schools for Dutch pre-university education were observed while performing 3 search tasks. The results indicate that subjects with WWW-experience are more proficient in locating Web sites than are novice WWW-users. The observed differences were ascribed to the experts' superior skills in operating Web search engines. However, on tasks that required subjects to locate information on specific Web sites, the performance of experienced and novice users was equivalent - a result that is in line with hypertext research. Based on these findings, implications for training and supporting students in searching for information on the WWW are identified. Finally, the role of the subjects' level of domain expertise is discussed and directions for future research are proposed

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Date

19. 3.1996 11:22:12

Source

Journal of documentation. 29(1973) no.3, S.251-257

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Abstract

Relevance has been a difficult concept to define, let alone measure. In this paper, a simple operational definition of relevance is proposed for a Web-based library catalog: whether or not during a search session the user saves, prints, mails, or downloads a citation. If one of those actions is performed, the session is considered relevant to the user. An analysis is presented illustrating the advantages and disadvantages of this definition. With this definition and good transaction logging, it is possible to ascertain the relevance of a session. This was done for 905,970 sessions conducted with the University of California's Melvyl online catalog. Next, a methodology was developed to try to predict the relevance of a session. A number of variables were defined that characterize a session, none of which used any demographic information about the user. The values of the variables were computed for the sessions. Principal components analysis was used to extract a new set of variables out of the original set. A stratified random sampling technique was used to form ten strata such that each new strata of 90,570 sessions contained the same proportion of relevant to nonrelevant sessions. Logistic regression was used to ascertain the regression coefficients for nine of the ten strata. Then, the coefficients were used to predict the relevance of the sessions in the missing strata. Overall, 17.85% of the sessions were determined to be relevant. The predicted number of relevant sessions for all ten strata was 11 %, a 6.85% difference. The authors believe that the methodology can be further refined and the prediction improved. This methodology could also have significant application in improving user searching and also in predicting electronic commerce buying decisions without the use of personal demographic data

Date

29. 9.2001 17:26:02

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Abstract

The Text REtrieval Conference (TREC), a yearly workshop hosted by the US government's National Institute of Standards and Technology, provides the infrastructure necessary for large-scale evaluation of text retrieval methodologies. With the goal of accelerating research in this area, TREC created the first large test collections of full-text documents and standardized retrieval evaluation. The impact has been significant; since TREC's beginning in 1992, retrieval effectiveness has approximately doubled. TREC has built a variety of large test collections, including collections for such specialized retrieval tasks as cross-language retrieval and retrieval of speech. Moreover, TREC has accelerated the transfer of research ideas into commercial systems, as demonstrated in the number of retrieval techniques developed in TREC that are now used in Web search engines. This book provides a comprehensive review of TREC research, summarizing the variety of TREC results, documenting the best practices in experimental information retrieval, and suggesting areas for further research. The first part of the book describes TREC's history, test collections, and retrieval methodology. Next, the book provides "track" reports -- describing the evaluations of specific tasks, including routing and filtering, interactive retrieval, and retrieving noisy text. The final part of the book offers perspectives on TREC from such participants as Microsoft Research, University of Massachusetts, Cornell University, University of Waterloo, City University of New York, and IBM. The book will be of interest to researchers in information retrieval and related technologies, including natural language processing.

Content

Enthält die Beiträge: 1. The Text REtrieval Conference - Ellen M. Voorhees and Donna K. Harman 2. The TREC Test Collections - Donna K. Harman 3. Retrieval System Evaluation - Chris Buckley and Ellen M. Voorhees 4. The TREC Ad Hoc Experiments - Donna K. Harman 5. Routing and Filtering - Stephen Robertson and Jamie Callan 6. The TREC Interactive Tracks: Putting the User into Search - Susan T. Dumais and Nicholas J. Belkin 7. Beyond English - Donna K. Harman 8. Retrieving Noisy Text - Ellen M. Voorhees and John S. Garofolo 9.The Very Large Collection and Web Tracks - David Hawking and Nick Craswell 10. Question Answering in TREC - Ellen M. Voorhees 11. The University of Massachusetts and a Dozen TRECs - James Allan, W. Bruce Croft and Jamie Callan 12. How Okapi Came to TREC - Stephen Robertson 13. The SMART Project at TREC - Chris Buckley 14. Ten Years of Ad Hoc Retrieval at TREC Using PIRCS - Kui-Lam Kwok 15. MultiText Experiments for TREC - Gordon V. Cormack, Charles L. A. Clarke, Christopher R. Palmer and Thomas R. Lynam 16. A Language-Modeling Approach to TREC - Djoerd Hiemstra and Wessel Kraaij 17. BM Research Activities at TREC - Eric W. Brown, David Carmel, Martin Franz, Abraham Ittycheriah, Tapas Kanungo, Yoelle Maarek, J. Scott McCarley, Robert L. Mack, John M. Prager, John R. Smith, Aya Soffer, Jason Y. Zien and Alan D. Marwick Epilogue: Metareflections on TREC - Karen Sparck Jones

Date

29. 3.1996 18:16:49

Footnote

Rez. in: JASIST 58(2007) no.6, S.910-911 (J.L. Vicedo u. J. Gomez): "The Text REtrieval Conference (TREC) is a yearly workshop hosted by the U.S. government's National Institute of Standards and Technology (NIST) that fosters and supports research in information retrieval as well as speeding the transfer of technology between research labs and industry. Since 1992, TREC has provided the infrastructure necessary for large-scale evaluations of different text retrieval methodologies. TREC impact has been very important and its success has been mainly supported by its continuous adaptation to the emerging information retrieval needs. Not in vain, TREC has built evaluation benchmarks for more than 20 different retrieval problems such as Web retrieval, speech retrieval, or question-answering. The large and intense trajectory of annual TREC conferences has resulted in an immense bulk of documents reflecting the different eval uation and research efforts developed. This situation makes it difficult sometimes to observe clearly how research in information retrieval (IR) has evolved over the course of TREC. TREC: Experiment and Evaluation in Information Retrieval succeeds in organizing and condensing all this research into a manageable volume that describes TREC history and summarizes the main lessons learned. The book is organized into three parts. The first part is devoted to the description of TREC's origin and history, the test collections, and the evaluation methodology developed. The second part describes a selection of the major evaluation exercises (tracks), and the third part contains contributions from research groups that had a large and remarkable participation in TREC. Finally, Karen Spark Jones, one of the main promoters of research in IR, closes the book with an epilogue that analyzes the impact of TREC on this research field.

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Abstract

This study sought to investigate the effects of cognitive style (field dependent and field independent) and on-line database search experience (novice and experienced) on the WWW search performance of undergraduate college students (n=48). It also attempted to find user factors that could be used to predict search efficiency. search performance, the dependent variable was defined in 2 ways: (1) time required for retrieving a relevant information item, and (2) the number of nodes traversed for retrieving a relevant information item. the search tasks required were carried out on a University Web site, and included a factual task and a topical search task of interest to the participant. Results indicated that while cognitive style (FD/FI) significantly influenced the search performance of novice searchers, the influence was greatly reduced in those searchers who had on-line database search experience. Based on the findings, suggestions for possible changes to the design of the current Web interface and to user training programs are provided