Document ranking based upon Markov chains

Danilowicz, Czesław; Baliński, Jaroslaw

doi:10.1016/s0306-4573(00)00038-8

Cited by 18 publications

(8 citation statements)

References 19 publications

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“…Kendall's coefficient of concordance τ (X, Y ) can be evaluated from the following formula (Daniłowicz and Baliński, 2001;Fagin et al, 2003;Kazienko and Kuźmińska, 2005;Kendall, 1948):…”

Section: 2mentioning

confidence: 99%

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Mining Indirect Association Rules for Web Recommendation

Kazienko¹

2009

International Journal of Applied Mathematics and Computer Science

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Classical association rules, here called "direct", reflect relationships existing between items that relatively often co-occur in common transactions. In the web domain, items correspond to pages and transactions to user sessions. The main idea of the new approach presented is to discover indirect associations existing between pages that rarely occur together but there are other, "third" pages, called transitive, with which they appear relatively frequently. Two types of indirect associations rules are described in the paper: partial indirect associations and complete ones. The former respect single transitive pages, while the latter cover all existing transitive pages. The presented IDARM* Algorithm extracts complete indirect association rules with their important measure-confidence-using pre-calculated direct rules. Both direct and indirect rules are joined into one set of complex association rules, which may be used for the recommendation of web pages. Performed experiments revealed the usefulness of indirect rules for the extension of a typical recommendation list. They also deliver new knowledge not available to direct ones. The relation between ranking lists created on the basis of direct association rules as well as hyperlinks existing on web pages is also examined.

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Section: 2mentioning

confidence: 99%

“…For the same n-item rankings X and Y , the Spearman's coefficient σ(X, Y ) is expressed in the following way (Daniłowicz and Baliński, 2001;Fagin et al, 2003;Spearman, 1904Spearman, /1987:…”

Section: 2mentioning

confidence: 99%

Mining Indirect Association Rules for Web Recommendation

Kazienko¹

2009

International Journal of Applied Mathematics and Computer Science

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“…There is a large body of work on re-ranking an initially retrieved list using graphbased methods that model inter-document similarities within the list (e.g., Dani lowicz & Baliński, 2000;Baliński & Dani lowicz, 2005;Diaz, 2005;Kurland & Lee, 2005Zhang et al, 2005;Yang, Ji, Zhou, Nie, & Xiao, 2006). As mentioned in Section 2, our fusion methods could conceptually be viewed as a generalization of some of these approaches (Dani lowicz & Baliński, 2000;Diaz, 2005;Kurland & Lee, 2005); specifically, of methods that utilize both retrieval scores and inter-document-similarities for modeling relevancestatus propagation within the list (Dani lowicz & Baliński, 2000;Diaz, 2005). A similar relevance-status propagation method was also employed in work on sentence retrieval for question answering (Otterbacher, Erkan, & Radev, 2005).…”

Section: Related Workmentioning

confidence: 99%

From “Identical” to “Similar”: Fusing Retrieved Lists Based on Inter-document Similarities

Kozorovitsky

Kurland

2009

Lecture Notes in Computer Science

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Methods for fusing document lists that were retrieved in response to a query often utilize the retrieval scores and/or ranks of documents in the lists. We present a novel fusion approach that is based on using, in addition, information induced from inter-document similarities. Specifically, our methods let similar documents from different lists provide relevance-status support to each other. We use a graph-based method to model relevancestatus propagation between documents. The propagation is governed by inter-documentsimilarities and by retrieval scores of documents in the lists. Empirical evaluation demonstrates the effectiveness of our methods in fusing TREC runs. The performance of our most effective methods transcends that of effective fusion methods that utilize only retrieval scores or ranks.1. We use the term "overlap" to refer to the number of documents shared by the retrieved lists rather than in reference to content overlap.

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“…Few researchers focus specifically on information retrieval: Chen and Cooper (2002) studied stochastic modeling of use patterns. Miller, Leek and Schwartz (1999) and Daniłowicz and Baliński (2001) focused on standard vector/idf·tw information retrieval. Other work emphasizes analyzing the language of documents for retrieval, such as Szepesvári and Littman (1996).…”

Section: Is As a Markov Processmentioning

confidence: 99%

Weighted Markov chains and graphic state nodes for information retrieval

Benoît

2002

Proc of Assoc for Info

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Decision-making in uncertain environments, such as data mining, involves a computer user navigating through multiple steps, from initial submission of a query through evaluating retrieval results, determining degrees of acceptability of the results, and advancing to a terminal state of evaluating where the interaction is successful or not. This paper describes iterative information seeking (IS) as a Markov process during which users advance through states of "nodes". Nodes are graphic objects on a computer screen that represent both the state of the system and the group of users' or an individual user's degree of confidence in an individual node. After examining nodes to establish a confidence level, the system records the decision as weights affecting the probability of the transition paths between nodes. By training the system in this way, the model incorporates into the underlying Markov process users' decisions as a means to reduce uncertainty. The Markov chain becomes a weighted one whereby the I S makes justified suggestions.

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Document ranking based upon Markov chains

Cited by 18 publications

References 19 publications

Mining Indirect Association Rules for Web Recommendation

Mining Indirect Association Rules for Web Recommendation

From “Identical” to “Similar”: Fusing Retrieved Lists Based on Inter-document Similarities

Weighted Markov chains and graphic state nodes for information retrieval

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