Using Markov models for web site link prediction

Zhu, Jianhan; Jiang, Hong; Hughes, John G.

doi:10.1145/513378.513381

Cited by 31 publications

(25 citation statements)

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“…But most importantly probabilistic sequence generation models like Markov chain can tackle all problems in relation with HTTP request anticipation. [6].…”

Section: A Application In Web Page Anticipationmentioning

confidence: 99%

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Analysis of Markov Model and Its Application in Web Page Anticipation

sorout¹

2017

ijarcs

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Abstract:In digital era of information where accessing information in limited time frame is necessary, then Markov model helps in the anticipation of the web page. Various network obstacles and minimum bandwidth of network can be efficiently used by employing Markov model and its clustering and pre-fetching technique. Markov Model and its clustering mechanisms helps us to make clusters of similar web users having based on same specified constraints and after the application of the Anticipation framework necessary web page can be accessed.

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“…But most importantly probabilistic sequence generation models like Markov chain can tackle all problems in relation with HTTP request anticipation. [6].…”

Section: A Application In Web Page Anticipationmentioning

confidence: 99%

“…In this whole process of the Anticipation Markov model is used as the adaptive model which changes its data structure and mechanism according to the user demands. [6].…”

Section: A Application In Web Page Anticipationmentioning

confidence: 99%

Analysis of Markov Model and Its Application in Web Page Anticipation

sorout¹

2017

ijarcs

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“…al. [32] proposed CitationCluster algorithm using co-citation and coupling similarity between web pages to conceptually cluster them. The algorithm is applied on a Markov model in order to construct a conceptual hierarchy and support link prediction.…”

Section: Related Workmentioning

confidence: 99%

Web path recommendations based on page ranking and Markov models

Eirinaki

Vazirgiannis

Kapogiannis

2005

Proceedings of the 7th Annual ACM International Workshop on Web Information and Data Management

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Markov models have been widely used for modelling users' navigational behaviour in the Web graph, using the transitional probabilities between web pages, as recorded in the web logs. The recorded users' navigation is used to extract popular web paths and predict current users' next steps. Such purely usage-based probabilistic models, however, present certain shortcomings. Since the prediction of users' navigational behaviour is based solely on the usage data, structural properties of the Web graph are ignored. Thus important -in terms of pagerank authority score -paths may be underrated. In this paper we present a hybrid probabilistic predictive model extending the properties of Markov models by incorporating link analysis methods. More specifically, we propose the use of a PageRank-style algorithm for assigning prior probabilities to the web pages based on their importance in the web site's graph. We prove, through experimentation, that this approach results in more objective and representative predictions than the ones produced from the pure usage-based approaches.

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“…It is particularly difficult to perform the discovery of missing or developing links in a certain network of interest (Liben-Nowell and Kleinberg 2007). However, link prediction is very useful to help: infer the underlying complete network (from partially observed structures) (Marchette and Priebe 2008;Kim and Leskovec 2011), understand the evolution of networks (Bringmann et al 2010;Barabâsi et al 2002) and predict hyperlinks in heterogeneous social networks (Zhu et al 2002). Traditionally, most of the approaches for detecting unobserved links are based on topological information, including neighbour-based metrics, path-based metrics and random walk-based metrics (Wang et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Inter-variable correlation prediction with fuzzy connected-triples

Shang

Shen

2018

Soft Comput

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Identification of hidden relationships between domain attributes from different data sources is of great practical significance and forms an emerging field in data mining. However, currently there seldom exist any systematic methods that can effectively handle this problem, especially when dealing with imprecisely described associations. In this paper, a novel data-driven approach for inter-variable correlation prediction is proposed by exploiting the concept of connected-triples. The work is implemented with the use of fuzzy logic. Through the exploitation of link strength measurements and fuzzy inference, the job of detecting similar or related variables can be accomplished via examining link relation patterns within and across different data sources. Empirical evaluation results are discussed, revealing the potential of the proposed work in predicting interesting attribute relations, while involving simple computation mechanisms.

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Using Markov models for web site link prediction

Cited by 31 publications

References 0 publications

Analysis of Markov Model and Its Application in Web Page Anticipation

Analysis of Markov Model and Its Application in Web Page Anticipation

Web path recommendations based on page ranking and Markov models

Inter-variable correlation prediction with fuzzy connected-triples

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