Using Markov Chains for Link Prediction in Adaptive Web Sites

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

doi:10.1007/3-540-46019-5_5

Cited by 95 publications

(48 citation statements)

References 10 publications

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“…In addition, the link prediction algorithms can also be used to generate some artificial links to help the further network analysis, such as the classification problem in partially labeled networks [11,5]. Some algorithms based on Markov chains [19,23,2] and machine learning [16,20] have been proposed recently, and another group of algorithms are based on the definition of node similarity. In this paper, we concentrate on the latter.…”

Section: Introductionmentioning

confidence: 99%

Role of weak ties in link prediction of complex networks

Lü

Zhou

2009

Proceedings of the 1st ACM International Workshop on Complex Networks Meet Information &Amp; Knowledge Management

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Plenty of algorithms for link prediction have been proposed and were applied to various real networks. Among these works, the weights of links are rarely taken into account. In this paper, we use local similarity indices to estimate the likelihood of the existence of links in weighted networks, including Common Neighbor, Adamic-Adar Index, Resource Allocation Index, and their weighted versions. In both the unweighted and weighted cases, the resource allocation index performs the best. To our surprise, the weighted indices perform worse, which reminds us of the well-known Weak Tie Theory. Further experimental study shows that the weak ties play a significant role in the link prediction problem, and to emphasize the contribution of weak ties can remarkably enhance the predicting accuracy.

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Section: Introductionmentioning

confidence: 99%

Role of weak ties in link prediction of complex networks

Lü

Zhou

2009

Proceedings of the 1st ACM International Workshop on Complex Networks Meet Information &Amp; Knowledge Management

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“…This system provides an objective behavior for user navigation. Jianhan Zhu et al [9] used the Markov chains to model user"s navigational behavior. They proposed a method for building a Markov model of a Website based on previous users"behavior.…”

Section: Related Workmentioning

confidence: 99%

Extracting Users’Navigational Behavior from Web Log Data: a Survey

Jafari¹,

SoleymaniSabzchi²,

Jamali³

2013

JCSA

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Web Usage Mining (WUM) is a kind of data mining method that can be used to discover user access patterns from Web log data. A lot of research has been done already about this area and the obtained results are used in different applications such as recommending the Web usage patterns, personalization, system improvement and business intelligence. WUM includes three phases that are called preprocessing, pattern discovery and pattern analysis. There are different techniques for WUM that have their own advantages and disadvantages. This paper presents a survey on some of the existing WUM techniques and it is shown that how WUM can be applied to Web server logs.

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“…al. [31] propose an improvement of the Sarukkai method, by computing the probability of the event of a user arriving in a state of the transition probability matrix within the next m steps. Even though both approaches address the problem of m-path prediction, the proposed methods are very demanding in terms of computational cost.…”

Section: Related Workmentioning

confidence: 99%

“…The 1 st -order Markov models (Markov Chains) provide a simple way to capture sequential dependence [5,9,27,31], but do not take into consideration the "long-term memory" aspects of web surfing behavior since they are based on the assumption that the next state to be visited is only a function of the current one. Higher-order Markov models [21] are more accurate for predicting navigational paths, there exists, however, a trade-off between improved coverage and exponential increase in statespace complexity as the order increases.…”

Section: Introductionmentioning

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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Using Markov Chains for Link Prediction in Adaptive Web Sites

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Cited by 95 publications

References 10 publications

Role of weak ties in link prediction of complex networks

Role of weak ties in link prediction of complex networks

Extracting Users’Navigational Behavior from Web Log Data: a Survey

Web path recommendations based on page ranking and Markov models

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