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Evaluation of Web-Based Search Engines Using User-Effort Measures
Evaluation of Web-Based Search Engines Using User-Effort Measures Muh-Chyun Tang and Ying Sun 4 Huntington St. School of Information, Communication and Library Studies Rutgers University, New Brunswick, NJ 08901, U.S.A. [email protected] [email protected] Abstract This paper presents a study of the applicability of three user-effort-sensitive evaluation measures —“first 20 full precision,” “search length,” and “rank correlation”—on four Web-based search engines (Google, AltaVista, Excite and Metacrawler). The authors argue that these measures are better alternatives than precision and recall in Web search situations because of their emphasis on the quality of ranking. Eight sets of search topics were collected from four Ph.D. students in four different disciplines (biochemistry, industrial engineering, economics, and urban planning). Each participant was asked to provide two topics along with the corresponding query terms. Their relevance and credibility judgment of the Web pages were then used to compare the performance of the search engines using these three measures. The results show consistency among these three ranking evaluation measures, more so between “first 20 full precision” and search length than between rank correlation and the other two measures. Possible reasons for rank correlation’s disagreement with the other two measures are discussed. Possible future research to improve these measures is also addressed. Introduction The explosive growth of information on the World Wide Web poses a challenge to traditional information retrieval (IR) research. Other than the sheer amount of information, some structural factors make searching for relevant and quality information on the Web a formidable task. -
How to Choose a Search Engine Or Directory
How to Choose a Search Engine or Directory Fields & File Types If you want to search for... Choose... Audio/Music AllTheWeb | AltaVista | Dogpile | Fazzle | FindSounds.com | Lycos Music Downloads | Lycos Multimedia Search | Singingfish Date last modified AllTheWeb Advanced Search | AltaVista Advanced Web Search | Exalead Advanced Search | Google Advanced Search | HotBot Advanced Search | Teoma Advanced Search | Yahoo Advanced Web Search Domain/Site/URL AllTheWeb Advanced Search | AltaVista Advanced Web Search | AOL Advanced Search | Google Advanced Search | Lycos Advanced Search | MSN Search Search Builder | SearchEdu.com | Teoma Advanced Search | Yahoo Advanced Web Search File Format AllTheWeb Advanced Web Search | AltaVista Advanced Web Search | AOL Advanced Search | Exalead Advanced Search | Yahoo Advanced Web Search Geographic location Exalead Advanced Search | HotBot Advanced Search | Lycos Advanced Search | MSN Search Search Builder | Teoma Advanced Search | Yahoo Advanced Web Search Images AllTheWeb | AltaVista | The Amazing Picture Machine | Ditto | Dogpile | Fazzle | Google Image Search | IceRocket | Ixquick | Mamma | Picsearch Language AllTheWeb Advanced Web Search | AOL Advanced Search | Exalead Advanced Search | Google Language Tools | HotBot Advanced Search | iBoogie Advanced Web Search | Lycos Advanced Search | MSN Search Search Builder | Teoma Advanced Search | Yahoo Advanced Web Search Multimedia & video All TheWeb | AltaVista | Dogpile | Fazzle | IceRocket | Singingfish | Yahoo Video Search Page Title/URL AOL Advanced -
Instrumentalizing the Sources of Attraction. How Russia Undermines Its Own Soft Power
INSTRUMENTALIZING THE SOURCES OF ATTRACTION. HOW RUSSIA UNDERMINES ITS OWN SOFT POWER By Vasile Rotaru Abstract The 2011-2013 domestic protests and the 2013-2015 Ukraine crisis have brought to the Russian politics forefront an increasing preoccupation for the soft power. The concept started to be used in official discourses and documents and a series of measures have been taken both to avoid the ‘dangers’ of and to streamline Russia’s soft power. This dichotomous approach towards the ‘power of attraction’ have revealed the differences of perception of the soft power by Russian officials and the Western counterparts. The present paper will analyse Russia’s efforts to control and to instrumentalize the sources of soft power, trying to assess the effectiveness of such an approach. Keywords: Russian soft power, Russian foreign policy, public diplomacy, Russian mass media, Russian internet Introduction The use of term soft power is relatively new in the Russian political circles, however, it has become recently increasingly popular among the Russian analysts, policy makers and politicians. The term per se was used for the first time in Russian political discourse in February 2012 by Vladimir Putin. In the presidential election campaign, the then candidate Putin drew attention to the fact that soft power – “a set of tools and methods to achieve foreign policy goals without the use of arms but by exerting information and other levers of influence” is used frequently by “big countries, international blocks or corporations” “to develop and provoke extremist, separatist and nationalistic attitudes, to manipulate the public and to directly interfere in the domestic policy of sovereign countries” (Putin 2012). -
Download on Our Platform and We Have Obtained Licenses from Many Content Providers
Table of Contents UNITED STATES SECURITIES AND EXCHANGE COMMISSION Washington, D.C. 20549 Form 20-F (Mark One) ¨ REGISTRATION STATEMENT PURSUANT TO SECTION 12(b) OR 12(g) OF THE SECURITIES EXCHANGE ACT OF 1934 or x ANNUAL REPORT PURSUANT TO SECTION 13 OR 15(d) OF THE SECURITIES EXCHANGE ACT OF 1934 For the fiscal year ended December 31, 2013. or ¨ TRANSITION REPORT PURSUANT TO SECTION 13 OR 15(d) OF THE SECURITIES EXCHANGE ACT OF 1934 For the transition period from to or ¨ SHELL COMPANY REPORT PURSUANT TO SECTION 13 OR 15(d) OF THE SECURITIES EXCHANGE ACT OF 1934 Date of event requiring this shell company report Commission file number: 000-51469 Baidu, Inc. (Exact name of Registrant as specified in its charter) N/A (Translation of Registrant’s name into English) Cayman Islands (Jurisdiction of incorporation or organization) Baidu Campus No. 10 Shangdi 10th Street Haidian District, Beijing 100085 The People’s Republic of China (Address of principal executive offices) Jennifer Xinzhe Li, Chief Financial Officer Telephone: +(86 10) 5992-8888 Email: [email protected] Facsimile: +(86 10) 5992-0000 Baidu Campus No. 10 Shangdi 10th Street, Haidian District, Beijing 100085 The People’s Republic of China (Name, Telephone, Email and/or Facsimile number and Address of Company Contact Person) Securities registered or to be registered pursuant to Section 12(b) of the Act: Title of Each Class Name of Each Exchange on Which Registered American depositary shares (ten American depositary shares representing one Class A ordinary share, The NASDAQ Stock Market LLC par value US$0.00005 per share) (The NASDAQ Global Select Market) Class A ordinary shares, par value US$0.00005 per share* The NASDAQ Stock Market LLC (The NASDAQ Global Select Market) * Not for trading, but only in connection with the listing on The NASDAQ Global Select Market of American depositary shares. -
Applications: S
Applications: S This chapter contains the following sections: • Sabah, on page 9 • Safari, on page 10 • SAFT, on page 11 • Sage, on page 12 • Sahibinden, on page 13 • Saks Fifth Avenue, on page 14 • Salesforce.com, on page 15 • Salesforce.com Live Agent, on page 16 • Sam's Club, on page 17 • Sametime, on page 18 • SAMR, on page 19 • Samsung, on page 20 • Samsung Push Notification, on page 21 • SANity, on page 22 • Sanook.com, on page 23 • SAP, on page 24 • SAP HostControl, on page 25 • SASCDN, on page 26 • SATNET, on page 27 • SATNET and Backroom EXPAK, on page 28 • SATNET Monitoring, on page 29 • SaveFrom, on page 30 • Sberbank of Russia, on page 31 • SBS, on page 32 • SCC Security, on page 33 • SCCM, on page 34 • SCCM Remote Control, on page 35 • SCCP, on page 36 • Schedule Transfer Protocol, on page 37 • schuelerVZ, on page 38 • Schwab, on page 39 • ScienceDirect, on page 40 Applications: S 1 Applications: S • SCO Desktop Administration Server, on page 41 • Sco I2 Dialog Daemon, on page 42 • SCO System Administration Server, on page 43 • SCO Web Server Manager 3, on page 44 • SCO WebServer Manager, on page 45 • scohelp, on page 46 • Scopia, on page 47 • Scopia Audio, on page 48 • Scopia Video, on page 49 • Scorecard Research, on page 50 • Scottrade, on page 51 • SCPS, on page 52 • Scribd, on page 53 • Scribd Upload, on page 54 • Scribol, on page 55 • SCSI-ST, on page 56 • SCTP, on page 57 • scx-proxy, on page 58 • SDNS-KMP, on page 59 • SDRP, on page 60 • Seamonkey, on page 61 • Search-Result.com, on page 62 • Searchnu, on page 63 • -
User Browsing Graph: Structure, Evolution and Application1
User Browsing Graph: Structure, Evolution and Application1 Yiqun Liu, Min Zhang, Shaoping Ma, Liyun Ru State Key Lab of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University, Beijing, 100084, China P.R. [email protected] ABSTRACT don’t work very well in this new situation. This paper focuses on ‘user browsing graph’ which is constructed Recently, the wisdom of the crowd is paid much attention in with users’ click-through behavior modeled with Web access logs. Web search researches, e.g. [7], [8] and [9]. In their work, users’ User browsing graph has recently been adopted to improve Web browsing behavior is usually considered as implicit feedback search performance and the initial study shows it is more reliable information for page relevance and importance. For example, Liu than hyperlink graph for inferring page importance. However, et. al. constructed ‘user browsing graph’ with search log data [10]. structure and evolution of the user browsing graph haven’t been They proposed a page importance estimation algorithm called fully studied and many questions remain to be answered. In this BrowseRank which performs on the user browsing graph. It is paper, we look into the structure of the user browsing graph and believed that the link structure in user browsing graph is more its evolution over time. We try to give a quantitative analysis on reliable than hyperlink graph because users actually follow links the difference in graph structure between hyperlink graph and in the browsing graph. user browsing graph, and then find out why link analysis Liu’s initial study shows that the BrowseRank algorithm works algorithms perform better on the browsing graph. -
Functional Differences Between Northern Light Singlepoint And
Functional Differences Between Northern Light SinglePoint and Microsoft SharePoint For Strategic Research Portals Northern Light® Whitepaper April 2011 TABLE OF CONTENTS Background On Northern Light SinglePoint ................................................................................................................... 2 Third-Party Licensed External Content ...................................................................................................................... 3 Internally Produced Research Content ...................................................................................................................... 4 User Authentication................................................................................................................................................... 4 Differences Between Northern Light SinglePoint and Microsoft SharePoint ................................................................ 5 Investment in a Research-optimized Portal and Supporting Systems ....................................................................... 5 Ability To Technically Integrate With Third-Parties To Index Research Content ....................................................... 6 Content Liability ......................................................................................................................................................... 7 Document Security Conventions Reflecting Licensing Arrangements ....................................................................... 7 Benefit of Having the -
Thème : Le Web Les Moteurs De Recherche Moteur De Recherche
Thème : Le Web Les moteurs de recherche Capacités attendues : - Comprendre le fonctionnement des moteurs de recherche - Mener une analyse critique des résultats d’un moteur de recherche - Comprendre les enjeux de la publication d’informations Moteur de recherche : application informatique permettant de rechercher une ressource (page Web, image, vidéo, fichier…) à partir d’une requête sous forme de mots-clés. 1. Le fonctionnement des moteurs de recherche * Au brouillon, essayez de schématiser le fonctionnement d’un moteur de recherche. Vocabulaire à retenir : - crawlers : robot d’indexation qui explore automatiquement le Web en suivant les liens entre les différentes pages pour collecter les ressources. - indexation : les mots-clés sont listés, classés et enregistrés sur des serveurs qui stockent les données. - pertinence : répond au besoin au moment où il est exprimé. Le Web est un immense graphe : chaque page est un noeud relié à d’autres nœuds par des liens hypertextes. On pourrait le schématiser de façon très simplifiée, comme ci-contre. Mme Suaudeau SNT 2019-2020 * A partir du nœud E, parcourez le graphe précédant en listant les pages consultées et les pages à visiter à chaque étape : Action Pages visitées Pages à visiter On visite E, lié à A et H E A H On visite A , lié à B E A H B On visite H , lié à J E A H B J On visite B , lié à A et C E A H B J C (A déjà visité) On visite J , lié à F E A H B J C F On visite C , lié à A E A H B J C C F (A déjà visité) On visite F , lié à C D G H E A H B J C F D G (C et H déjà visités) On visite D , lié à / E A H B J C F D G On visite G , lié à / E A H B J C F D G * Que se passe-t-il si l’on commence l’exploration par le nœud A ? La visite est terminée en 3 étapes mais on a « raté » une grande partie du graphe. -
An Empirical Analysis of Internet Search Engine Choice
An Empirical Analysis of Internet Search Engine Choice Rahul Telang ([email protected]) Tridas Mukhopadhyay ([email protected]) Ronald T. Wilcox ([email protected]) Send correspondence to Rahul Telang H. John Heinz III School of Public Policy and Management Carnegie Mellon University 5000 Forbes Avenue Pittsburgh, PA 15213-3890 [email protected] December 2001 An Empirical Analysis of Internet Search Engine Choice Abstract We investigate consumers’ choice behavior for Internet search engines. Within this broad agenda, we focus on two interrelated issues. First, we examine whether consumers develop loyalty to a particular search engine. If loyalty does indeed develop, we seek to understand the role of loyalty in the search engine choice. We also explore how the use of non-search features such as email, news, etc. provided by the engines enhances or inhibits customer loyalty. Second, we seek to determine how search engine performance affects the user choice behavior. To accomplish our research objective, we first develop a conceptual model of search engine choice based on the literature of human-computer interaction and cognitive psychology. Our model reflects the fact that information goods such as search engines are a fundamentally different class of products than common household items. We posit that the ability to learn various search engine features and the ease (or difficulty) of transferring this learning to other engines would determine loyalty in this context. Indeed, we expect the user to exhibit differing levels of loyalty to search and non-search features of the engines. We also expect that dissatisfaction with search results would negatively affect search engine choice. -
Meta Search Engine Examples
Meta Search Engine Examples mottlesMarlon istemerariously unresolvable or and unhitches rice ichnographically left. Salted Verney while crowedanticipated no gawk Horst succors underfeeding whitherward and naphthalising. after Jeremy Chappedredetermines and acaudalfestively, Niels quite often sincipital. globed some Schema conflict can be taken the meta descriptions appear after which result, it later one or can support. Would result for updating systematic reviews from different business view all fields need to our generated usually negotiate the roi. What is hacking or hacked content? This meta engines! Search Engines allow us to filter the tons of information available put the internet and get the bid accurate results And got most people don't. Best Meta Search array List The Windows Club. Search engines have any category, google a great for a suggestion selection has been shown in executive search input from health. Search engine name of their booking on either class, the sites can select a search and generally, meaning they have past the systematisation of. Search Engines Corner Meta-search Engines Ariadne. Obsession of search engines such as expedia, it combines the example, like the answer about search engines out there were looking for. Test Embedded Software IC Design Intellectual Property. Using Research Tools Web Searching OCLS. The meta description for each browser settings to bing, boolean logic always prevent them the hierarchy does it displays the search engine examples osubject directories. Online travel agent Bookingcom has admitted that playing has trouble to compensate customers whose personal details have been stolen Guests booking hotel rooms have unwittingly handed over business to criminals Bookingcom is go of the biggest online travel agents. -
Sogou Announces Fourth Quarter and Full Year 2020 Results BEIJING, China, February 4, 2021 – Sogou Inc. (NYSE: SOGO) ("So
Sogou Announces Fourth Quarter and Full Year 2020 Results BEIJING, China, February 4, 2021 – Sogou Inc. (NYSE: SOGO) ("Sogou" or the “Company"), an innovator in search and a leader in China's internet industry, today announced its unaudited financial results for the fourth quarter and full year, ended December 31, 2020. Fourth Quarter 2020 Financial Results Total revenues1 were $189.5 million, a 37% decrease year-over-year. The decrease was primarily driven by uncertainties with respect to Sogou’s business policies among certain advertisers as a result of the previously-announced proposal by Tencent Holdings Limited (“Tencent”) to take Sogou private, as well as reduced traffic acquisition activity. • Search and search-related revenues were $166.7 million, a 39% decrease year- over-year. Auction-based pay-for-click services decreased year-over-year, accounting for 84.0% of search and search-related revenues, compared to 88.2% in the corresponding period in 2019. • Other revenues were $22.8 million, a 14% decrease year-over-year. The decrease was primarily due to decreased revenues from non-core businesses. Cost of revenues was $151.2 million, a 10% decrease year-over-year. Traffic acquisition cost, a primary driver of cost of revenues, was $115.5 million, a 10% decrease year-over-year, representing 60.9% of total revenues, compared to 42.8% in the corresponding period in 2019. The decrease in traffic acquisition costs was driven by decreased traffic acquisition from third parties. Gross profit and non-GAAP2 gross profit were both $38.3 million, a 71% decrease year- over-year for both. -
Is Google the Next Microsoft? Competition, Welfare and Regulation in Online Search
IS GOOGLE THE NEXT MICROSOFT? COMPETITION, WELFARE AND REGULATION IN ONLINE SEARCH RUFUS POLLOCK UNIVERSITY OF CAMBRIDGE Abstract. The rapid growth of online search and its centrality to the ecology of the Internet pose important questions: why is the search engine market so concentrated and will it evolve towards monopoly? What implications does this concentration have for consumers, search engines, and advertisers? Does search require regulation and if so in what form? This paper supplies empirical and theoretical material with which to examine these questions. In particular, we (a) show that the already large levels of concentration are likely to continue (b) identify the consequences, negative and positive, of this outcome (c) discuss the regulatory interventions that policy-makers could use to address these. Keywords: Search Engine, Regulation, Competition, Antitrust, Platform Markets JEL Classification: L40 L10 L50 Emmanuel College and Faculty of Economics, University of Cambridge. Email: [email protected] or [email protected]. I thank the organizers of 2007 IDEI Toulouse Conference on the Economics of the Software and Internet Industries whose search round-table greatly stimulated my thoughts on this topic as well as participants at seminars in Cambridge and at the 2009 RES conference. Particular thanks is also due to the editor and anonymous referee who provided an excellent set of comments that served to greatly improve the paper. Finally, I would like to thank http://searchenginewatch.com and Hitwise who both generously provided me with access to some of their data. This work is licensed under a Creative Commons Attribution Licence (v3.0, all jurisdictions).