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  1. Li, L.; Shang, Y.; Zhang, W.: Improvement of HITS-based algorithms on Web documents 0.47
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    Content
    Vgl.: http%3A%2F%2Fdelab.csd.auth.gr%2F~dimitris%2Fcourses%2Fir_spring06%2Fpage_rank_computing%2Fp527-li.pdf. Vgl. auch: http://www2002.org/CDROM/refereed/643/.
  2. Dominich, S.; Skrop, A.: PageRank and interaction information retrieval (2005) 0.02
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    Abstract
    The PageRank method is used by the Google Web search engine to compute the importance of Web pages. Two different views have been developed for the Interpretation of the PageRank method and values: (a) stochastic (random surfer): the PageRank values can be conceived as the steady-state distribution of a Markov chain, and (b) algebraic: the PageRank values form the eigenvector corresponding to eigenvalue 1 of the Web link matrix. The Interaction Information Retrieval (1**2 R) method is a nonclassical information retrieval paradigm, which represents a connectionist approach based an dynamic systems. In the present paper, a different Interpretation of PageRank is proposed, namely, a dynamic systems viewpoint, by showing that the PageRank method can be formally interpreted as a particular case of the Interaction Information Retrieval method; and thus, the PageRank values may be interpreted as neutral equilibrium points of the Web.
  3. Roux, M.: Metadata for search engines : what can be learned from e-Sciences? (2012) 0.01
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    Abstract
    E-sciences are data-intensive sciences that make a large use of the Web to share, collect, and process data. In this context, primary scientific data is becoming a new challenging issue as data must be extensively described (1) to account for empiric conditions and results that allow interpretation and/or analyses and (2) to be understandable by computers used for data storage and information retrieval. With this respect, metadata is a focal point whatever it is considered from the point of view of the user to visualize and exploit data as well as this of the search tools to find and retrieve information. Numerous disciplines are concerned with the issues of describing complex observations and addressing pertinent knowledge. In this paper, similarities and differences in data description and exploration strategies among disciplines in e-sciences are examined.
  4. 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.
  5. MacLeod, R.: Promoting a subject gateway : a case study from EEVL (Edinburgh Engineering Virtual Library) (2000) 0.01
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    Date
    22. 6.2002 19:40:22
  6. Vidmar, D.J.: Darwin on the Web : the evolution of search tools (1999) 0.01
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    Source
    Computers in libraries. 19(1999) no.5, S.22-28
  7. Back, J.: ¬An evaluation of relevancy ranking techniques used by Internet search engines (2000) 0.01
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    Date
    25. 8.2005 17:42:22
  8. Dunning, A.: Do we still need search engines? (1999) 0.01
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    Source
    Ariadne. 1999, no.22
  9. Bawden, D.: Google and the universe of knowledge (2008) 0.01
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    Date
    7. 6.2008 16:22:20
  10. Birmingham, J.: Internet search engines (1996) 0.00
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    Date
    10.11.1996 16:36:22
  11. Hock, R.E.: How to do field searching in Web search engines : a field trip (1998) 0.00
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    Source
    Online. 22(1998) no.3, S.18-22
  12. Price, A.: Five new Danish subject gateways under development (2000) 0.00
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    Date
    22. 6.2002 19:41:31
  13. Conhaim, W.W.: Search tools (1996) 0.00
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    Date
    1. 8.1996 22:39:31
  14. Fong, W.W.: Searching the World Wide Web (1996) 0.00
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    Source
    Journal of library and information science. 22(1996) no.1, S.15-36
  15. Duval, B.K.; Main, L.: Searching on the Net : general overview (1996) 0.00
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    Date
    6. 3.1997 16:22:15
  16. Notess, G.R.: Toward more comprehensive Web searching : single searching versus megasearching (1998) 0.00
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    Source
    Online. 22(1998) no.2, S.73-76
  17. Berinstein, P.: Turning visual : image search engines on the Web (1998) 0.00
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    Source
    Online. 22(1998) no.3, S.37-38,40-42
  18. Amato, G.; Rabitti, F.; Savino, P.: Multimedia document search on the Web (1998) 0.00
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    Date
    1. 8.1996 22:08:06
  19. Gardner, T.; Iannella, R.: Architecture and software solutions (2000) 0.00
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    Date
    22. 6.2002 19:38:24
  20. Peereboom, M.: DutchESS : Dutch Electronic Subject Service - a Dutch national collaborative effort (2000) 0.00
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    Date
    22. 6.2002 19:39:23

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