Search (24 results, page 1 of 2)

  • × language_ss:"e"
  • × theme_ss:"Suchmaschinen"
  • × year_i:[2010 TO 2020}
  1. Fluhr, C.: Crosslingual access to photo databases (2012) 0.01
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    Abstract
    This paper is about search of photos in photo databases of agencies which sell photos over the Internet. The problem is far from the behavior of photo databases managed by librarians and also far from the corpora generally used for research purposes. The descriptions use mainly single words and it is well known that it is not the best way to have a good search. This increases the problem of semantic ambiguity. This problem of semantic ambiguity is crucial for cross-language querying. On the other hand, users are not aware of documentation techniques and use generally very simple queries but want to get precise answers. This paper gives the experience gained in a 3 year use (2006-2008) of a cross-language access to several of the main international commercial photo databases. The languages used were French, English, and German.
    Date
    17. 4.2012 14:25:22
  2. 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.
    Source
    Journal of discrete algorithms. 8(2010) no.2, S.199-213
    Year
    2010
  3. Shapira, B.; Zabar, B.: Personalized search : integrating collaboration and social networks (2011) 0.00
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    Abstract
    Despite improvements in their capabilities, search engines still fail to provide users with only relevant results. One reason is that most search engines implement a "one size fits all" approach that ignores personal preferences when retrieving the results of a user's query. Recent studies (Smyth, 2010) have elaborated the importance of personalizing search results and have proposed integrating recommender system methods for enhancing results using contextual and extrinsic information that might indicate the user's actual needs. In this article, we review recommender system methods used for personalizing and improving search results and examine the effect of two such methods that are merged for this purpose. One method is based on collaborative users' knowledge; the second integrates information from the user's social network. We propose new methods for collaborative-and social-based search and demonstrate that each of these methods, when separately applied, produce more accurate search results than does a purely keyword-based search engine (referred to as "standard search engine"), where the social search engine is more accurate than is the collaborative one. However, separately applied, these methods do not produce a sufficient number of results (low coverage). Nevertheless, merging these methods with those implemented by standard search engines overcomes the low-coverage problem and produces personalized results for users that display significantly more accurate results while also providing sufficient coverage than do standard search engines. The improvement, however, is significant only for topics for which the diversity of terms used for queries among users is low.
  4. Bouidghaghen, O.; Tamine, L.: Spatio-temporal based personalization for mobile search (2012) 0.00
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    Abstract
    The explosion of the information available on the Internet has made traditional information retrieval systems, characterized by one size fits all approaches, less effective. Indeed, users are overwhelmed by the information delivered by such systems in response to their queries, particularly when the latter are ambiguous. In order to tackle this problem, the state-of-the-art reveals that there is a growing interest towards contextual information retrieval (CIR) which relies on various sources of evidence issued from the user's search background and environment, in order to improve the retrieval accuracy. This chapter focuses on mobile context, highlights challenges they present for IR, and gives an overview of CIR approaches applied in this environment. Then, the authors present an approach to personalize search results for mobile users by exploiting both cognitive and spatio-temporal contexts. The experimental evaluation undertaken in front of Yahoo search shows that the approach improves the quality of top search result lists and enhances search result precision.
    Date
    20. 4.2012 13:19:22
  5. Croft, W.B.; Metzler, D.; Strohman, T.: Search engines : information retrieval in practice (2010) 0.00
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    Classification
    TK 5105.884 .C765 2010
    LCC
    TK 5105.884 .C765 2010
    Year
    2010
  6. Chau, M.; Wong, C.H.; Zhou, Y.; Qin, J.; Chen, H.: Evaluating the use of search engine development tools in IT education (2010) 0.00
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    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.2, S.288-299
    Year
    2010
  7. Fu, T.; Abbasi, A.; Chen, H.: ¬A focused crawler for Dark Web forums (2010) 0.00
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    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.6, S.1213-1231
    Year
    2010
  8. Clewley, N.; Chen, S.Y.; Liu, X.: Cognitive styles and search engine preferences : field dependence/independence vs holism/serialism (2010) 0.00
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    Source
    Journal of documentation. 66(2010) no.4, S.585-603
    Year
    2010
  9. Joint, N.: ¬The one-stop shop search engine : a transformational library technology? ANTAEUS (2010) 0.00
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    Source
    Library review. 59(2010) no.4, S.240-248
    Year
    2010
  10. Kopp, O.: Google Hummingbird-Algorithmus-Update : Infos & Hintergründe (2013) 0.00
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    Abstract
    Pünktlich zum 15. Geburtstag der Google Suche verkündete Google gestern auf einer Pressekonferenz in der "Gründungs-Garage", dass das bedeutendste Google Update seit dem Caffeine Update im Jahr 2010 und größte Algorithmus-Update seit 2001 schon seit ca. einem Monat aktiv ist. Das aktuelle Update heißt Hummingbird zu deutsch Kollibri. Es soll ca. 90% aller Suchanfragen betreffen und soll im Vergleich zu Caffeine ein echtes Algorithmus-Update sein. Es soll dabei helfen komplexere Suchanfragen besser zu deuten und noch besser die eigentliche Suchintention bzw. Fragestellung hinter einer Suchanfrage zu erkennen sowie passende Dokumente dazu anzubieten. Auch auf Dokumentenebene soll die eigentliche Intention hinter dem Content besser mit der Suchanfrage gematcht werden.
  11. Bilal, D.; Gwizdka, J.: Children's query types and reformulations in Google search (2018) 0.00
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    Abstract
    We investigated the searching behaviors of twenty-four children in grades 6, 7, and 8 (ages 11-13) in finding information on three types of search tasks in Google. Children conducted 72 search sessions and issued 150 queries. Children's phrase- and question-like queries combined were much more prevalent than keyword queries (70% vs. 30%, respectively). Fifty two percent of the queries were reformulations (33 sessions). We classified children's query reformulation types into five classes based on the taxonomy by Liu et al. (2010). We found that most query reformulations were by Substitution and Specialization, and that children hardly repeated queries. We categorized children's queries by task facets and examined the way they expressed these facets in their query formulations and reformulations. Oldest children tended to target the general topic of search tasks in their queries most frequently, whereas younger children expressed one of the two facets more often. We assessed children's achieved task outcomes using the search task outcomes measure we developed. Children were mostly more successful on the fact-finding and fully self-generated task and partially successful on the research-oriented task. Query type, reformulation type, achieved task outcomes, and expressing task facets varied by task type and grade level. There was no significant effect of query length in words or of the number of queries issued on search task outcomes. The study findings have implications for human intervention, digital literacy, search task literacy, as well as for system intervention to support children's query formulation and reformulation during interaction with Google.
  12. 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.
  13. Luo, M.M.; Nahl, D.: Let's Google : uncertainty and bilingual search (2019) 0.00
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    Abstract
    This study applies Kuhlthau's Information Search Process stage (ISP) model to understand bilingual users' Internet search experience. We conduct a quasi-field experiment with 30 bilingual searchers and the results suggested that the ISP model was applicable in studying searchers' information retrieval behavior in search tasks. The ISP model was applicable in studying searchers' information retrieval behavior in simple tasks. However, searchers' emotional responses differed from those of the ISP model for a complex task. By testing searchers using different search strategies, the results suggested that search engines with multilanguage search functions provide an advantage for bilingual searchers in the Internet's multilingual environment. The findings showed that when searchers used a search engine as a tool for problem solving, they might experience different feelings in each ISP stage than in searching for information for a term paper using a library. The results echo other research findings that indicate that information seeking is a multifaceted phenomenon.
  14. Bensman, S.J.: Eugene Garfield, Francis Narin, and PageRank : the theoretical bases of the Google search engine (2013) 0.00
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    Date
    17.12.2013 11:02:22
  15. 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
  16. 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
  17. Chaudiron, S.; Ihadjadene, M.: Studying Web search engines from a user perspective : key concepts and main approaches (2012) 0.00
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    20. 4.2012 13:22:37
  18. Lewandowski, D.; Spree, U.: Ranking of Wikipedia articles in search engines revisited : fair ranking for reasonable quality? (2011) 0.00
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    Date
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  19. Aloteibi, S.; Sanderson, M.: Analyzing geographic query reformulation : an exploratory study (2014) 0.00
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    Date
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  20. Vaughan, L.; Chen, Y.: Data mining from web search queries : a comparison of Google trends and Baidu index (2015) 0.00
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    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22