Search (25 results, page 1 of 2)

  • × language_ss:"e"
  • × theme_ss:"Suchmaschinen"
  • × type_ss:"a"
  • × year_i:[2010 TO 2020}
  1. Lewandowski, D.; Spree, U.: Ranking of Wikipedia articles in search engines revisited : fair ranking for reasonable quality? (2011) 0.02 
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    Date
    30. 9.2012 19:27:22
  2. Lewandowski, D.; Sünkler, S.: What does Google recommend when you want to compare insurance offerings? (2019) 0.02 
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    Date
    20. 1.2015 18:30:22
  3. 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
  4. Fluhr, C.: Crosslingual access to photo databases (2012) 0.01 
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    Date
    17. 4.2012 14:25:22
  5. Chen, L.-C.: Next generation search engine for the result clustering technology (2012) 0.01 
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    Date
    17. 4.2012 15:22:11
  6. 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
  7. 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
  8. 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
  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. 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
  13. Lewandowski, D.; Kerkmann, F.; Rümmele, S.; Sünkler, S.: ¬An empirical investigation on search engine ad disclosure (2018) 0.00 
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    Location
    D
  14. Kruschwitz, U.; Lungley, D.; Albakour, M-D.; Song, D.: Deriving query suggestions for site search (2013) 0.00 
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  15. Lewandowski, D.: ¬A framework for evaluating the retrieval effectiveness of search engines (2012) 0.00 
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  16. Lewandowski, D.: Evaluating the retrieval effectiveness of web search engines using a representative query sample (2015) 0.00 
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  17. Luo, M.M.; Nahl, D.: Let's Google : uncertainty and bilingual search (2019) 0.00 
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  18. 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.
  19. Lewandowski, D.: ¬The retrieval effectiveness of search engines on navigational queries (2011) 0.00 
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  20. Lewandowski, D.; Drechsler, J.; Mach, S. von: Deriving query intents from web search engine queries (2012) 0.00 
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