Search (9 results, page 1 of 1)

  • × year_i:[2010 TO 2020}
  • × theme_ss:"Retrievalstudien"
  1. Balog, K.; Schuth, A.; Dekker, P.; Tavakolpoursaleh, N.; Schaer, P.; Chuang, P.-Y.: Overview of the TREC 2016 Open Search track Academic Search Edition (2016) 0.03 
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    Abstract
    We present the TREC Open Search track, which represents a new evaluation paradigm for information retrieval. It offers the possibility for researchers to evaluate their approaches in a live setting, with real, unsuspecting users of an existing search engine. The first edition of the track focuses on the academic search domain and features the ad-hoc scientific literature search task. We report on experiments with three different academic search engines: Cite-SeerX, SSOAR, and Microsoft Academic Search.
  2. Sarigil, E.; Sengor Altingovde, I.; Blanco, R.; Barla Cambazoglu, B.; Ozcan, R.; Ulusoy, Ö.: Characterizing, predicting, and handling web search queries that match very few or no results (2018) 0.02 
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    Abstract
    A non-negligible fraction of user queries end up with very few or even no matching results in leading commercial web search engines. In this work, we provide a detailed characterization of such queries and show that search engines try to improve such queries by showing the results of related queries. Through a user study, we show that these query suggestions are usually perceived as relevant. Also, through a query log analysis, we show that the users are dissatisfied after submitting a query that match no results at least 88.5% of the time. As a first step towards solving these no-answer queries, we devised a large number of features that can be used to identify such queries and built machine-learning models. These models can be useful for scenarios such as the mobile- or meta-search, where identifying a query that will retrieve no results at the client device (i.e., even before submitting it to the search engine) may yield gains in terms of the bandwidth usage, power consumption, and/or monetary costs. Experiments over query logs indicate that, despite the heavy skew in class sizes, our models achieve good prediction quality, with accuracy (in terms of area under the curve) up to 0.95.
  3. Schaer, P.; Mayr, P.; Sünkler, S.; Lewandowski, D.: How relevant is the long tail? : a relevance assessment study on million short (2016) 0.02 
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    Abstract
    Users of web search engines are known to mostly focus on the top ranked results of the search engine result page. While many studies support this well known information seeking pattern only few studies concentrate on the question what users are missing by neglecting lower ranked results. To learn more about the relevance distributions in the so-called long tail we conducted a relevance assessment study with the Million Short long-tail web search engine. While we see a clear difference in the content between the head and the tail of the search engine result list we see no statistical significant differences in the binary relevance judgments and weak significant differences when using graded relevance. The tail contains different but still valuable results. We argue that the long tail can be a rich source for the diversification of web search engine result lists but it needs more evaluation to clearly describe the differences.
  4. Reichert, S.; Mayr, P.: Untersuchung von Relevanzeigenschaften in einem kontrollierten Eyetracking-Experiment (2012) 0.00 
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    Date
    22. 7.2012 19:25:54
  5. Pal, S.; Mitra, M.; Kamps, J.: Evaluation effort, reliability and reusability in XML retrieval (2011) 0.00 
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    Date
    22. 1.2011 14:20:56
  6. Chu, H.: Factors affecting relevance judgment : a report from TREC Legal track (2011) 0.00 
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    Date
    12. 7.2011 18:29:22
  7. Wildemuth, B.; Freund, L.; Toms, E.G.: Untangling search task complexity and difficulty in the context of interactive information retrieval studies (2014) 0.00 
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    Date
    6. 4.2015 19:31:22
  8. Ravana, S.D.; Taheri, M.S.; Rajagopal, P.: Document-based approach to improve the accuracy of pairwise comparison in evaluating information retrieval systems (2015) 0.00 
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    Date
    20. 1.2015 18:30:22
  9. Rajagopal, P.; Ravana, S.D.; Koh, Y.S.; Balakrishnan, V.: Evaluating the effectiveness of information retrieval systems using effort-based relevance judgment (2019) 0.00 
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    Date
    20. 1.2015 18:30:22

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