Search (38 results, page 1 of 2)

  • × theme_ss:"Suchmaschinen"
  • × year_i:[2010 TO 2020}
  1. Niedermair, K.: Gefährden Suchmaschinen und Discovery-Systeme die informationelle Autonomie? (2014) 0.02 
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  2. Croft, W.B.; Metzler, D.; Strohman, T.: Search engines : information retrieval in practice (2010) 0.01 
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    Classification
    TWX (FH K)
    GHBS
    TWX (FH K)
  3. Lewandowski, D.: Suchmaschinen verstehen (2018) 0.01 
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    GHBS
    TVK (FH K)
  4. Lewandowski, D.: Suchmaschinen verstehen (2015) 0.01 
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    Classification
    TVK (FH K)
    GHBS
    TVK (FH K)
  5. 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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  6. Lewandowski, D.: Query understanding (2011) 0.01 
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    Date
    18. 9.2018 18:22:18
  7. 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
  8. Tober, M.; Hennig, L.; Furch, D.: SEO Ranking-Faktoren und Rang-Korrelationen 2014 : Google Deutschland (2014) 0.01 
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    Date
    13. 9.2014 14:45:22
  9. Schaat, S.: Von der automatisierten Manipulation zur Manipulation der Automatisierung (2019) 0.01 
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    Date
    19. 2.2019 17:22:00
  10. Handbuch Internet-Suchmaschinen 2 : Neue Entwicklungen in der Web-Suche (2011) 0.01 
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    Classification
    TVK (FH K)
    GHBS
    TVK (FH K)
  11. Levy, S.: Google inside : Wie Google denkt, arbeitet und unser Leben verändert (2012) 0.01 
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    KNZ (FH K)
  12. Behnert, C.; Plassmeier, K.; Borst, T.; Lewandowski, D.: Evaluierung von Rankingverfahren für bibliothekarische Informationssysteme (2019) 0.01 
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  13. Ortiz-Cordova, A.; Jansen, B.J.: Classifying web search queries to identify high revenue generating customers (2012) 0.01 
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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.
  14. Flores-Herr, N.; Sack, H.; Bossert, K.: Suche in Multimediaarchiven von Kultureinrichtungen (2011) 0.01 
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  15. Vidinli, I.B.; Ozcan, R.: New query suggestion framework and algorithms : a case study for an educational search engine (2016) 0.01 
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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.
  16. Fluhr, C.: Crosslingual access to photo databases (2012) 0.01 
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    Date
    17. 4.2012 14:25:22
  17. Chen, L.-C.: Next generation search engine for the result clustering technology (2012) 0.01 
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    17. 4.2012 15:22:11
  18. Bouidghaghen, O.; Tamine, L.: Spatio-temporal based personalization for mobile search (2012) 0.01 
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    Date
    20. 4.2012 13:19:22
  19. Lewandowski, D.: ¬Die Macht der Suchmaschinen und ihr Einfluss auf unsere Entscheidungen (2014) 0.01 
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    Date
    22. 9.2014 18:54:11
  20. Huvila, I.: Affective capitalism of knowing and the society of search engine (2016) 0.01 
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    Date
    20. 1.2015 18:30:22

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