Search (38 results, page 2 of 2)

  • × theme_ss:"Suchmaschinen"
  • × year_i:[2010 TO 2020}
  1. ¬Die Googleisierung der Informationssuche : Suchmaschinen zwischen Nutzung und Regulierung (2014) 0.01 
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    Classification
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    GHBS
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  2. Bressan, M.; Peserico, E.: Choose the damping, choose the ranking? (2010) 0.01 
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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.
  3. Weber, K.: Search engine bias (2011) 0.01 
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  4. Bhansali, D.; Desai, H.; Deulkar, K.: ¬A study of different ranking approaches for semantic search (2015) 0.01 
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  5. Rieh, S.Y.; Kim, Y.-M.; Markey, K.: Amount of invested mental effort (AIME) in online searching (2012) 0.01 
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  6. Levy, S.: In the plex : how Google thinks, works, and shapes our lives (2011) 0.01 
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    Classification
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  7. Chaudiron, S.; Ihadjadene, M.: Studying Web search engines from a user perspective : key concepts and main approaches (2012) 0.01 
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    Date
    20. 4.2012 13:22:37
  8. Lewandowski, D.; Spree, U.: Ranking of Wikipedia articles in search engines revisited : fair ranking for reasonable quality? (2011) 0.01 
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    Date
    30. 9.2012 19:27:22
  9. Aloteibi, S.; Sanderson, M.: Analyzing geographic query reformulation : an exploratory study (2014) 0.01 
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    Date
    26. 1.2014 18:48:22
  10. Vaughan, L.; Chen, Y.: Data mining from web search queries : a comparison of Google trends and Baidu index (2015) 0.01 
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    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22
  11. Alqaraleh, S.; Ramadan, O.; Salamah, M.: Efficient watcher based web crawler design (2015) 0.01 
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    Date
    20. 1.2015 18:30:22
  12. epd: Kaiserslauterer Forscher untersuchen Google-Suche (2017) 0.01 
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    Date
    22. 7.2004 9:42:33
  13. Lewandowski, D.; Sünkler, S.: What does Google recommend when you want to compare insurance offerings? (2019) 0.01 
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    Date
    20. 1.2015 18:30:22
  14. Sachse, J.: ¬The influence of snippet length on user behavior in mobile web search (2019) 0.01 
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    Date
    20. 1.2015 18:30:22
  15. 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.
  16. Gillitzer, B.: Yewno (2017) 0.01 
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    Date
    22. 2.2017 10:16:49
  17. Gossen, T.: Search engines for children : search user interfaces and information-seeking behaviour (2016) 0.01 
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    Date
    1. 2.2016 18:25:22
  18. Averesch, D.: Googeln ohne Google : Mit alternativen Suchmaschinen gelingt ein neutraler Überblick (2010) 0.00 
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    Date
    3. 5.1997 8:44:22

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