Search (13 results, page 1 of 1)

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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

This study introduces methods for evaluating search engine performance over a time period. Several measures are defined, which as a whole describe search engine functionality over time. The necessary setup for such studies is described, and the use of these measures is illustrated through a specific example. The set of measures introduced here may serve as a guideline for the search engines for testing and improving their functionality. We recommend setting up a standard suite of measures for evaluating search engine performance.

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Abstract

The literature of the evaluation of Internet search engines is reviewed. Although there have been many studies, there has been little consistency in the way such studies have been carried out. This problem is exacerbated by the fact that recall is virtually impossible to calculate in the fast changing Internet environment, and therefore the traditional Cranfield type of evaluation is not usually possible. A variety of alternative evaluation methods has been suggested to overcome this difficulty. The authors recommend that a standardised set of tools is developed for the evaluation of web search engines so that, in future, comparisons can be made between search engines more effectively, and that variations in performance of any given search engine over time can be tracked. The paper itself does not provide such a standard set of tools, but it investigates the issues and makes preliminary recommendations of the types of tools needed

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Abstract

An empirical investigation of the consistency of retrieval through Internet search engines is reported. Thirteen engines are evaluated: AltaVista, EuroFerret, Excite, HotBot, InfoSeek, Lycos, MSN, NorthernLight, Snap, WebCrawler and three national Dutch engines: Ilse, Search.nl and Vindex. The focus is on a characteristics related to size: the degree of consistency to which an engine retrieves documents. Does an engine always present the same relevant documents that are, or were, available in its databases? We observed and identified three types of fluctuations in the result sets of several kinds of searches, many of them significant. These should be taken into account by users who apply an Internet search engine, for instance to retrieve as many relevant documents as possible, or to retrieve a document that was already found in a previous search, or to perform scientometric/bibliometric measurements. The fluctuations should also be considered as a complication of other research on the behaviour and performance of Internet search engines. In conclusion: in view of the increasing importance of the Internet as a publication/communication medium, the fluctuations in the result sets of Internet search engines can no longer be neglected.

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Abstract

Previous research has found that improved search engine effectiveness-evaluated using a batch-style approach-does not always translate to significant improvements in user task performance; however, these prior studies focused on simple recall and precision-based search tasks. We investigated the same relationship, but for realistic, complex search tasks required in clinical decision making. One hundred and nine clinicians and final year medical students answered 16 clinical questions. Although the search engine did improve answer accuracy by 20 percentage points, there was no significant difference when participants used a more effective, state-of-the-art search engine. We also found that the search engine effectiveness difference, identified in the lab, was diminished by around 70% when the search engines were used with real users. Despite the aid of the search engine, half of the clinical questions were answered incorrectly. We further identified the relative contribution of search engine effectiveness to the overall end task success. We found that the ability to interpret documents correctly was a much more important factor impacting task success. If these findings are representative, information retrieval research may need to reorient its emphasis towards helping users to better understand information, rather than just finding it for them.

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Abstract

Seven hundred natural language questions from TREC-8 and TREC-9 were sent by Radev, Libner, and Fan to each of nine web search engines. The top 40 sites returned by each system were stored for evaluation of their productivity of correct answers. Each question per engine was scored as the sum of the reciprocal ranks of identified correct answers. The large number of zero scores gave a positive skew violating the normality assumption for ANOVA, so values were transformed to zero for no hit and one for one or more hits. The non-zero values were then square-root transformed to remove the remaining positive skew. Interactions were observed between search engine and answer type (name, place, date, et cetera), search engine and number of proper nouns in the query, search engine and the need for time limitation, and search engine and total query words. All effects were significant. Shortest queries had the highest mean scores. One or more proper nouns present provides a significant advantage. Non-time dependent queries have an advantage. Place, name, person, and text description had mean scores between .85 and .9 with date at .81 and number at .59. There were significant differences in score by search engine. Search engines found at least one correct answer in between 87.7 and 75.45 of the cases. Google and Northern Light were just short of a 90% hit rate. No evidence indicated that a particular engine was better at answering any particular sort of question.

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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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Abstract

Users of web search engines are known to mostly focus on the top ranked results of the search engine result page. While many studies support this well known information seeking pattern only few studies concentrate on the question what users are missing by neglecting lower ranked results. To learn more about the relevance distributions in the so-called long tail we conducted a relevance assessment study with the Million Short long-tail web search engine. While we see a clear difference in the content between the head and the tail of the search engine result list we see no statistical significant differences in the binary relevance judgments and weak significant differences when using graded relevance. The tail contains different but still valuable results. We argue that the long tail can be a rich source for the diversification of web search engine result lists but it needs more evaluation to clearly describe the differences.

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Abstract

In this issue we begin with a paper where Eastman points out that conventional narrower queries (the use of conjunctions and phrases) in a web engine search will reduce returned number of hits but not necessarily increase precision in the top ranked documents in the return. Thus by precision anomalies Eastman means that search narrowing activity results in no precision change or a decrease in precision. Multiple queries with multiple engines were run by students for a three-year period and the formulation/engine combination was recorded as was the number of hits. Relevance was also recorded for the top ten and top twenty ranked retrievals. While narrower searches reduced total hits they did not usually improve precision. Initial high precision and poor query reformulation account for some of the results, as did Alta Vista's failure to use the ranking algorithm incorporated in its regular search in its advanced search feature. However, since the top listed returns often reoccurred in all formulations, it would seem that the ranking algorithms are doing a consistent job of practical precision ranking that is not improved by reformulation.

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Abstract

A real case study is shown, explaining in a timeline the whole process of design, development and evaluation of a search engine used as a navigational help tool for end users and clients an a content website, e-commerce driven. The nature of the website is a community website, which will determine the core design of the information service. This study will involve several steps, such as information recovery system analysis, comparative analysis of other commercial search engines, service design, functionalities and scope; software selection, design of the project, project management, future service administration and conclusions.

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Abstract

The objective of this paper is to highlight the importance of a scientifically sounded approach to search engine evaluation. Nowadays there is a flourishing literature which describes various attempts at conducting such evaluation by following all sort of approaches, but very often only the final results are published with little, if any, information about the methodology and the procedures adopted. These various experiments have been critically investigated and catalogued according to their scientific foundation by Bell [1] in the attempt to provide a valuable framework for future studies in this area. This paper reconsiders some of Bell's ideas in the light of the crisis of classic evaluation techniques for information retrieval and tries to envisage some form of collaboration between the IR and web communities in order to design a better and more consistent platform for the evaluation of tools for interactive information retrieval.

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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