Search (35 results, page 1 of 2)

  • × theme_ss:"Suchmaschinen"
  • × type_ss:"a"
  • × year_i:[2010 TO 2020}
  1. Kaeser, E.: ¬Das postfaktische Zeitalter (2016) 0.01 
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    Content
    "Es gibt Daten, Informationen und Fakten. Wenn man mir eine Zahlenreihe vorsetzt, dann handelt es sich um Daten: unterscheidbare Einheiten, im Fachjargon: Items. Wenn man mir sagt, dass diese Items stündliche Temperaturangaben der Aare im Berner Marzilibad bedeuten, dann verfüge ich über Information - über interpretierte Daten. Wenn man mir sagt, dies seien die gemessenen Aaretemperaturen am 22. August 2016 im Marzili, dann ist das ein Faktum: empirisch geprüfte interpretierte Daten. Dieser Dreischritt - Unterscheiden, Interpretieren, Prüfen - bildet quasi das Bindemittel des Faktischen, «the matter of fact». Wir alle führen den Dreischritt ständig aus und gelangen so zu einem relativ verlässlichen Wissen und Urteilsvermögen betreffend die Dinge des Alltags. Aber wie schon die Kurzcharakterisierung durchblicken lässt, bilden Fakten nicht den Felsengrund der Realität. Sie sind kritikanfällig, sowohl von der Interpretation wie auch von der Prüfung her gesehen. Um bei unserem Beispiel zu bleiben: Es kann durchaus sein, dass man uns zwei unterschiedliche «faktische» Temperaturverläufe der Aare am 22. August 2016 vorsetzt.
    Date
    24. 8.2016 9:29:24
  2. Niedermair, K.: Gefährden Suchmaschinen und Discovery-Systeme die informationelle Autonomie? (2014) 0.01 
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  3. Werner, K.: das Confirmation/Disconfirmation-Paradigma der Kundenzufriedenheit im Kontext des Information Retrieval : Größere Zufriedenheit durch bessere Suchmaschinen? (2010) 0.01 
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  4. Peters, I.: Folksonomies und kollaborative Informationsdienste : eine Alternative zur Websuche? (2011) 0.01 
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    Pages
    S.29-53
  5. Lewandowski, D.: Query understanding (2011) 0.01 
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    Date
    18. 9.2018 18:22:18
  6. Bensman, S.J.: Eugene Garfield, Francis Narin, and PageRank : the theoretical bases of the Google search engine (2013) 0.01 
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    Date
    17.12.2013 11:02:22
  7. Schaat, S.: Von der automatisierten Manipulation zur Manipulation der Automatisierung (2019) 0.01 
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    Date
    19. 2.2019 17:22:00
  8. Behnert, C.; Plassmeier, K.; Borst, T.; Lewandowski, D.: Evaluierung von Rankingverfahren für bibliothekarische Informationssysteme (2019) 0.00 
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  9. Franke-Maier, M.; Rüter, C.: Discover Sacherschließung! : Was machen suchmaschinenbasierte Systeme mit unseren inhaltlichen Metadaten? (2015) 0.00 
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    Date
    2. 3.2015 10:29:44
  10. Lewandowski, D.: Perspektiven eines Open Web Index (2016) 0.00 
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    Date
    16. 5.2016 21:53:29
  11. Ortiz-Cordova, A.; Jansen, B.J.: Classifying web search queries to identify high revenue generating customers (2012) 0.00 
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    Abstract
    Traffic from search engines is important for most online businesses, with the majority of visitors to many websites being referred by search engines. Therefore, an understanding of this search engine traffic is critical to the success of these websites. Understanding search engine traffic means understanding the underlying intent of the query terms and the corresponding user behaviors of searchers submitting keywords. In this research, using 712,643 query keywords from a popular Spanish music website relying on contextual advertising as its business model, we use a k-means clustering algorithm to categorize the referral keywords with similar characteristics of onsite customer behavior, including attributes such as clickthrough rate and revenue. We identified 6 clusters of consumer keywords. Clusters range from a large number of users who are low impact to a small number of high impact users. We demonstrate how online businesses can leverage this segmentation clustering approach to provide a more tailored consumer experience. Implications are that businesses can effectively segment customers to develop better business models to increase advertising conversion rates.
  12. Flores-Herr, N.; Sack, H.; Bossert, K.: Suche in Multimediaarchiven von Kultureinrichtungen (2011) 0.00 
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  13. Vidinli, I.B.; Ozcan, R.: New query suggestion framework and algorithms : a case study for an educational search engine (2016) 0.00 
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    Abstract
    Query suggestion is generally an integrated part of web search engines. In this study, we first redefine and reduce the query suggestion problem as "comparison of queries". We then propose a general modular framework for query suggestion algorithm development. We also develop new query suggestion algorithms which are used in our proposed framework, exploiting query, session and user features. As a case study, we use query logs of a real educational search engine that targets K-12 students in Turkey. We also exploit educational features (course, grade) in our query suggestion algorithms. We test our framework and algorithms over a set of queries by an experiment and demonstrate a 66-90% statistically significant increase in relevance of query suggestions compared to a baseline method.
  14. Ke, W.: Decentralized search and the clustering paradox in large scale information networks (2012) 0.00 
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    Pages
    S.29-46
  15. Fluhr, C.: Crosslingual access to photo databases (2012) 0.00 
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    Date
    17. 4.2012 14:25:22
  16. Chen, L.-C.: Next generation search engine for the result clustering technology (2012) 0.00 
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    Date
    17. 4.2012 15:22:11
  17. Bouidghaghen, O.; Tamine, L.: Spatio-temporal based personalization for mobile search (2012) 0.00 
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    Date
    20. 4.2012 13:19:22
  18. Lewandowski, D.: ¬Die Macht der Suchmaschinen und ihr Einfluss auf unsere Entscheidungen (2014) 0.00 
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    Date
    22. 9.2014 18:54:11
  19. Huvila, I.: Affective capitalism of knowing and the society of search engine (2016) 0.00 
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    Date
    20. 1.2015 18:30:22
  20. Bressan, M.; Peserico, E.: Choose the damping, choose the ranking? (2010) 0.00 
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    Abstract
    To what extent can changes in PageRank's damping factor affect node ranking? We prove that, at least on some graphs, the top k nodes assume all possible k! orderings as the damping factor varies, even if it varies within an arbitrarily small interval (e.g. [0.84999,0.85001][0.84999,0.85001]). Thus, the rank of a node for a given (finite set of discrete) damping factor(s) provides very little information about the rank of that node as the damping factor varies over a continuous interval. We bypass this problem introducing lineage analysis and proving that there is a simple condition, with a "natural" interpretation independent of PageRank, that allows one to verify "in one shot" if a node outperforms another simultaneously for all damping factors and all damping variables (informally, time variant damping factors). The novel notions of strong rank and weak rank of a node provide a measure of the fuzziness of the rank of that node, of the objective orderability of a graph's nodes, and of the quality of results returned by different ranking algorithms based on the random surfer model. We deploy our analytical tools on a 41M node snapshot of the .it Web domain and on a 0.7M node snapshot of the CiteSeer citation graph. Among other findings, we show that rank is indeed relatively stable in both graphs; that "classic" PageRank (d=0.85) marginally outperforms Weighted In-degree (d->0), mainly due to its ability to ferret out "niche" items; and that, for both the Web and CiteSeer, the ideal damping factor appears to be 0.8-0.9 to obtain those items of high importance to at least one (model of randomly surfing) user, but only 0.5-0.6 to obtain those items important to every (model of randomly surfing) user.

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