Search (192 results, page 1 of 10)

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Abstract

In this paper, we present two ways to improve the precision of HITS-based algorithms onWeb documents. First, by analyzing the limitations of current HITS-based algorithms, we propose a new weighted HITS-based method that assigns appropriate weights to in-links of root documents. Then, we combine content analysis with HITS-based algorithms and study the effects of four representative relevance scoring methods, VSM, Okapi, TLS, and CDR, using a set of broad topic queries. Our experimental results show that our weighted HITS-based method performs significantly better than Bharat's improved HITS algorithm. When we combine our weighted HITS-based method or Bharat's HITS algorithm with any of the four relevance scoring methods, the combined methods are only marginally better than our weighted HITS-based method. Between the four relevance scoring methods, there is no significant quality difference when they are combined with a HITS-based algorithm.

Content

Vgl.: http%3A%2F%2Fdelab.csd.auth.gr%2F~dimitris%2Fcourses%2Fir_spring06%2Fpage_rank_computing%2Fp527-li.pdf. Vgl. auch: http://www2002.org/CDROM/refereed/643/.

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Abstract

Previous work an search-engine design has indicated that information-seekers may benefit from being given the opportunity to exploit multiple sources of evidence of document relatedness. Few existing systems, however, give users more than minimal control over the selections that may be made among methods of exploitation. By applying the methods of "document network analysis" (DNA), a unifying, graph-theoretic model of content-, collaboration-, and context-based systems (CCC) may be developed in which the nature of the similarities between types of document relatedness and document ranking are clarified. The usefulness of the approach to system design suggested by this model may be tested by constructing and evaluating a prototype system (UCXtra) that allows searchers to maintain control over the multiple ways in which document collections may be ranked and re-ranked.

Date

11. 9.2004 17:32:22

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Content

"Sir, The Internet has become the means to obtain information or to transact business. Most of the research, including recent articles by Declan Butler (2006) and Kevin Yager (2006), note a demand for quality search content from the Web. User interactions with the Internet are performed through a Web browser. An average user usually browses through, at the most, thirty to forty links out of the total delivery set, which can be quite large, from a search engine. In the early 1990s, browsers were textual, with no graphical presentations available. Subsequently, browsers could display graphic content. Some browsers can view content in the style and font desired by the user, but with limited operability. However, these browsers do not inherit any intelligence from technological research to employ as an expert system for the user. In addition, search engines have been left on their own to grow with technology. On the other hand, using the same technology has complicated Web content. This may lead to the best search engines rarely doing poorly, and the worst ones rarely doing well, but any result is possible (Salganik, Dodds, & Watts, 2006). Active research is being done with search engines to address the abundance of technological development for quality delivery of content. Some search engines look at the country, language, browser technical details (e.g., version, compatibility), and other factors before delivery. However the missing factor is user characteristics.

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Abstract

This paper aims to review the fiercely discussed question of whether the ranking of Wikipedia articles in search engines is justified by the quality of the articles. After an overview of current research on information quality in Wikipedia, a summary of the extended discussion on the quality of encyclopedic entries in general is given. On this basis, a heuristic method for evaluating Wikipedia entries is developed and applied to Wikipedia articles that scored highly in a search engine retrieval effectiveness test and compared with the relevance judgment of jurors. In all search engines tested, Wikipedia results are unanimously judged better by the jurors than other results on the corresponding results position. Relevance judgments often roughly correspond with the results from the heuristic evaluation. Cases in which high relevance judgments are not in accordance with the comparatively low score from the heuristic evaluation are interpreted as an indicator of a high degree of trust in Wikipedia. One of the systemic shortcomings of Wikipedia lies in its necessarily incoherent user model. A further tuning of the suggested criteria catalog, for instance, the different weighing of the supplied criteria, could serve as a starting point for a user model differentiated evaluation of Wikipedia articles. Approved methods of quality evaluation of reference works are applied to Wikipedia articles and integrated with the question of search engine evaluation.

Date

30. 9.2012 19:27:22

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Abstract

Numerous studies have explored the possibility of uncovering information from web search queries but few have examined the factors that affect web query data sources. We conducted a study that investigated this issue by comparing Google Trends and Baidu Index. Data from these two services are based on queries entered by users into Google and Baidu, two of the largest search engines in the world. We first compared the features and functions of the two services based on documents and extensive testing. We then carried out an empirical study that collected query volume data from the two sources. We found that data from both sources could be used to predict the quality of Chinese universities and companies. Despite the differences between the two services in terms of technology, such as differing methods of language processing, the search volume data from the two were highly correlated and combining the two data sources did not improve the predictive power of the data. However, there was a major difference between the two in terms of data availability. Baidu Index was able to provide more search volume data than Google Trends did. Our analysis showed that the disadvantage of Google Trends in this regard was due to Google's smaller user base in China. The implication of this finding goes beyond China. Google's user bases in many countries are smaller than that in China, so the search volume data related to those countries could result in the same issue as that related to China.

Source

Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22

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Abstract

PageRank is defined as the stationary state of a Markov chain. The chain is obtained by perturbing the transition matrix induced by a web graph with a damping factor alpha that spreads uniformly part of the rank. The choice of alpha is eminently empirical, and in most cases the original suggestion alpha=0.85 by Brin and Page is still used. Recently, however, the behaviour of PageRank with respect to changes in alpha was discovered to be useful in link-spam detection. Moreover, an analytical justification of the value chosen for alpha is still missing. In this paper, we give the first mathematical analysis of PageRank when alpha changes. In particular, we show that, contrarily to popular belief, for real-world graphs values of alpha close to 1 do not give a more meaningful ranking. Then, we give closed-form formulae for PageRank derivatives of any order, and an extension of the Power Method that approximates them with convergence O(t**k*alpha**t) for the k-th derivative. Finally, we show a tight connection between iterated computation and analytical behaviour by proving that the k-th iteration of the Power Method gives exactly the PageRank value obtained using a Maclaurin polynomial of degree k. The latter result paves the way towards the application of analytical methods to the study of PageRank.

Date

16. 1.2016 10:22:28

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Abstract

Despite improvements in their capabilities, search engines still fail to provide users with only relevant results. One reason is that most search engines implement a "one size fits all" approach that ignores personal preferences when retrieving the results of a user's query. Recent studies (Smyth, 2010) have elaborated the importance of personalizing search results and have proposed integrating recommender system methods for enhancing results using contextual and extrinsic information that might indicate the user's actual needs. In this article, we review recommender system methods used for personalizing and improving search results and examine the effect of two such methods that are merged for this purpose. One method is based on collaborative users' knowledge; the second integrates information from the user's social network. We propose new methods for collaborative-and social-based search and demonstrate that each of these methods, when separately applied, produce more accurate search results than does a purely keyword-based search engine (referred to as "standard search engine"), where the social search engine is more accurate than is the collaborative one. However, separately applied, these methods do not produce a sufficient number of results (low coverage). Nevertheless, merging these methods with those implemented by standard search engines overcomes the low-coverage problem and produces personalized results for users that display significantly more accurate results while also providing sufficient coverage than do standard search engines. The improvement, however, is significant only for topics for which the diversity of terms used for queries among users is low.

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Date

7. 2.1996 22:38:41

Pages

22 S

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Date

22. 8.1999 20:22:06

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Date

22. 8.1999 21:22:59

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Abstract

Social phenomena happen, and the historians follow. So it goes with Google, the latest star shooting through the universe of trend-setting businesses. This company has even entered our popular lexicon: as many note, "Google" has moved beyond noun to verb, becoming an action which most tech-savvy citizens at the turn of the twenty-first century recognize and in fact do, on a daily basis. It's this wide societal impact that fascinated authors David Vise and Mark Malseed, who came to the book with well-established reputations in investigative reporting. Vise authored the bestselling The Bureau and the Mole, and Malseed contributed significantly to two Bob Woodward books, Bush at War and Plan of Attack. The kind of voluminous research and behind-the-scenes insight in which both writers specialize, and on which their earlier books rested, comes through in The Google Story. The strength of the book comes from its command of many small details, and its focus on the human side of the Google story, as opposed to the merely academic one. Some may prefer a dryer, more analytic approach to Google's impact on the Internet, like The Search or books that tilt more heavily towards bits and bytes on the spectrum between technology and business, like The Singularity is Near. Those wanting to understand the motivations and personal growth of founders Larry Page and Sergey Brin and CEO Eric Schmidt, however, will enjoy this book. Vise and Malseed interviewed over 150 people, including numerous Google employees, Wall Street analysts, Stanford professors, venture capitalists, even Larry Page's Cub Scout leader, and their comprehensiveness shows. As the narrative unfolds, readers learn how Google grew out of the intellectually fertile and not particularly directed friendship between Page and Brin; how the founders attempted to peddle early versions of their search technology to different Silicon Valley firms for $1 million; how Larry and Sergey celebrated their first investor's check with breakfast at Burger King; how the pair initially housed their company in a Palo Alto office, then eventually moved to a futuristic campus dubbed the "Googleplex"; how the company found its financial footing through keyword-targeted Web ads; how various products like Google News, Froogle, and others were cooked up by an inventive staff; how Brin and Page proved their mettle as tough businessmen through negotiations with AOL Europe and their controversial IPO process, among other instances; and how the company's vision for itself continues to grow, such as geographic expansion to China and cooperation with Craig Venter on the Human Genome Project. Like the company it profiles, The Google Story is a bit of a wild ride, and fun, too. Its first appendix lists 23 "tips" which readers can use to get more utility out of Google. The second contains the intelligence test which Google Research offers to prospective job applicants, and shows the sometimes zany methods of this most unusual business. Through it all, Vise and Malseed synthesize a variety of fascinating anecdotes and speculation about Google, and readers seeking a first draft of the history of the company will enjoy an easy read.

Date

3. 5.1997 8:44:22

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Content

Inhalt: Acknowledgments; Abstract; Zusammenfassung; Contents; List of Figures; List of Tables; List of Acronyms; Chapter 1 Introduction ; 1.1 Research Questions; 1.2 Thesis Outline; Part I Fundamentals ; Chapter 2 Information Retrieval for Young Users ; 2.1 Basics of Information Retrieval; 2.1.1 Architecture of an IR System; 2.1.2 Relevance Ranking; 2.1.3 Search User Interfaces; 2.1.4 Targeted Search Engines; 2.2 Aspects of Child Development Relevant for Information Retrieval Tasks; 2.2.1 Human Cognitive Development; 2.2.2 Information Processing Theory; 2.2.3 Psychosocial Development 2.3 User Studies and Evaluation2.3.1 Methods in User Studies; 2.3.2 Types of Evaluation; 2.3.3 Evaluation with Children; 2.4 Discussion; Chapter 3 State of the Art ; 3.1 Children's Information-Seeking Behaviour; 3.1.1 Querying Behaviour; 3.1.2 Search Strategy; 3.1.3 Navigation Style; 3.1.4 User Interface; 3.1.5 Relevance Judgement; 3.2 Existing Algorithms and User Interface Concepts for Children; 3.2.1 Query; 3.2.2 Content; 3.2.3 Ranking; 3.2.4 Search Result Visualisation; 3.3 Existing Information Retrieval Systems for Children; 3.3.1 Digital Book Libraries; 3.3.2 Web Search Engines 3.4 Summary and DiscussionPart II Studying Open Issues ; Chapter 4 Usability of Existing Search Engines for Young Users ; 4.1 Assessment Criteria; 4.1.1 Criteria for Matching the Motor Skills; 4.1.2 Criteria for Matching the Cognitive Skills; 4.2 Results; 4.2.1 Conformance with Motor Skills; 4.2.2 Conformance with the Cognitive Skills; 4.2.3 Presentation of Search Results; 4.2.4 Browsing versus Searching; 4.2.5 Navigational Style; 4.3 Summary and Discussion; Chapter 5 Large-scale Analysis of Children's Queries and Search Interactions; 5.1 Dataset; 5.2 Results; 5.3 Summary and Discussion Chapter 6 Differences in Usability and Perception of Targeted Web Search Engines between Children and Adults 6.1 Related Work; 6.2 User Study; 6.3 Study Results; 6.4 Summary and Discussion; Part III Tackling the Challenges ; Chapter 7 Search User Interface Design for Children ; 7.1 Conceptual Challenges and Possible Solutions; 7.2 Knowledge Journey Design; 7.3 Evaluation; 7.3.1 Study Design; 7.3.2 Study Results; 7.4 Voice-Controlled Search: Initial Study; 7.4.1 User Study; 7.5 Summary and Discussion; Chapter 8 Addressing User Diversity ; 8.1 Evolving Search User Interface 8.1.1 Mapping Function8.1.2 Evolving Skills; 8.1.3 Detection of User Abilities; 8.1.4 Design Concepts; 8.2 Adaptation of a Search User Interface towards User Needs; 8.2.1 Design & Implementation; 8.2.2 Search Input; 8.2.3 Result Output; 8.2.4 General Properties; 8.2.5 Configuration and Further Details; 8.3 Evaluation; 8.3.1 Study Design; 8.3.2 Study Results; 8.3.3 Preferred UI Settings; 8.3.4 User satisfaction; 8.4 Knowledge Journey Exhibit; 8.4.1 Hardware; 8.4.2 Frontend; 8.4.3 Backend; 8.5 Summary and Discussion; Chapter 9 Supporting Visual Searchers in Processing Search Results 9.1 Related Work

Date

1. 2.2016 18:25:22

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Date

27.11.2005 18:04:22

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Abstract

The relationship between the methods currently used for indexing the World Wide Web and the programs, languages, and protocols on which the World Wide Web is based is examined. Two methods for indexing the Web are described, directories being briefly discussed while search engines are considered in detail. The automated approach used to create these tools is examined with special emphasis on the parts of a document used in indexing. Shortcomings of the approach are described. Suggestions for effective use of Web search engines are given

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Abstract

Search engines are defined, and recent developments described, exemplified, and evaluated, especially those concerned with traditional search and retrieval capabilities. Discussion concentrates on two broad issues: (1) collection and indexing methods and (2) retrieval and ranking methods. It is concluded that a wider adoption of field searching, proximity searching, and relevance feedback would improve quality of search results

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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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Date

22. 6.2002 19:40:22

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Source

Computers in libraries. 19(1999) no.5, S.22-28

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Date

25. 8.2005 17:42:22

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Date

3. 5.1997 8:44:22