Data Mining of User Navigation Patterns

Borges, José; Levene, Mark

doi:10.1007/3-540-44934-5_6

Cited by 199 publications

(161 citation statements)

References 16 publications

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“…Our contribution lies primarily in the initial hypothesis with which these algorithms work. We tried starting from the standard probabilistic prefix tree automaton (PPTA) as well as the so called hypertext probabilistic automaton (HPA) [6]. The latter type of automaton has been previously used in web usage mining to locate the most frequently accessed paths in a web site [5] but not within the paradigm of grammatical inference.…”

Section: Grammatical Inference Methods For Sequence Miningmentioning

confidence: 99%

Navigation Pattern Discovery Using Grammatical Inference

Karampatziakis

Παλιούρας

Pierrakos

et al. 2004

Grammatical Inference: Algorithms and Applications

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Abstract. We present a method for modeling user navigation on a web site using grammatical inference of stochastic regular grammars. With this method we achieve better models than the previously used first order Markov chains, in terms of predictive accuracy and utility of recommendations. In order to obtain comparable results, we apply the same grammatical inference algorithms on Markov chains, modeled as probabilistic automata. The automata induced in this way perform better than the original Markov chains, as models for user navigation, but they are considerably inferior to the automata induced by the traditional grammatical inference methods. The evaluation of our method was based on two web usage data sets from two very dissimilar web sites. It consisted in producing, for each user, a personalized list of recommendations and then measuring its recall and expected utility.

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Section: Grammatical Inference Methods For Sequence Miningmentioning

confidence: 99%

Navigation Pattern Discovery Using Grammatical Inference

Karampatziakis

Παλιούρας

Pierrakos

et al. 2004

Grammatical Inference: Algorithms and Applications

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“…Magdalini Eirinaki and Michalis Vazirgiannisrely on the application of statistical analysis and intelligent data mining methods (for instance, clustering, association rule mining, sequential pattern discovery and classification) to the Web log data, resulting in a set of valuable patterns that imply individuals' access patterns, and the knowledge is then employed to personalize pages for users according to their navigational behavior and profile [9]. Based on the theory of probability, Borges and Levene put forward a data mining method that captures users' web page access patterns: individuals' navigation sessions are treated as hypertext probabilistic grammar whose higher probability strings correspond to the interested tails of an individual and the last N visited web pages affect the affect the probability of the following page to be navigated [4]. Ezeife and Lu proposed a Web access pattern tree (WAPtree) approach to explore frequent visit sequences for users, which can response dynamically without numerous re-constructions of WAP-tree during knowledge mining [10].…”

Section: Web Page Navigation Logs Miningmentioning

confidence: 99%

Mining Users’ Preference Similarities in E-commerce Systems Based on Webpage Navigation Logs

Zhang

et al. 2017

INT J COMPUT COMMUN

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Mining users' preference patterns in e-commerce systems is a fertile area for a great many application directions, such as shopping intention analysis, prediction and personalized recommendation. The web page navigation logs contain much potentially useful information, and provide opportunities for understanding the correlation between users' browsing patterns and what they want to buy. In this article, we propose a web browsing history mining based user preference discovery method for e-commerce systems. First of all, a user-browsing-history-hierarchical-presentationgraph to established to model the web browsing histories of an individual in common e-commerce systems, and secondly an interested web page detection algorithm is designed to extract users' preference. Finally, a new method called UPSAWBH (User Preference Similarity Calculation Algorithm Based on Web Browsing History), which measure the level of users' preference similarity on the basis of their web page click patterns, is put forward. In the proposed UPSAWBH, we take two factors into account: 1) the number of shared web page click sequence, and 2) the property of the clicked web page that reflects users' shopping preference in e-commerce systems. We conduct experiments on real dataset, which is extracted from the server of our self-developed e-commerce system. The results indicate a good effectiveness of the proposed approach.

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“…The problem of finding the MPESs, the most difficult part of the CES discovery process, maps nicely to sequence mining discovery [5,11,8,22,24] which is a standard approach for finding sequential patterns in a dataset. Web usage mining (e.g., [8,22,26,18,23]) is a sub-area within the area of sequence mining particularly applicable to the MPES-discovery problem.…”

Section: Related Workmentioning

confidence: 99%

A fast method for discovering critical edge sequences in e-commerce catalogs

Dutta

VanderMeer

Datta

et al. 2007

European Journal of Operational Research

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Web sites allow the collection of vast amounts of navigational data -clickstreams of user traversals through the site. These massive data stores offer the tantalizing possibility of uncovering interesting patterns within the dataset. For e-businesses, always looking for an edge in the hypercompetitive online marketplace, the discovery of Critical Edge Sequences (CESs), which denote frequently traversed sequences in the catalog, is of significant interest. CESs can be used to improve site performance and site management, increase the effectiveness of advertising on the site, and gather additional knowledge of customer behavior patterns on the site.Using web mining strategies to find CESs turns out to be expensive in both space and time. In this paper, we propose an approximate algorithm to compute the most popular traversal sequences between node pairs in a catalog, which are then used to discover CESs. Our method is both fast and space efficient, providing a vast reduction in both the run time and storage requirements, with minimum impact on accuracy.

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Data Mining of User Navigation Patterns

Cited by 199 publications

References 16 publications

Navigation Pattern Discovery Using Grammatical Inference

Navigation Pattern Discovery Using Grammatical Inference

Mining Users’ Preference Similarities in E-commerce Systems Based on Webpage Navigation Logs

A fast method for discovering critical edge sequences in e-commerce catalogs

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