In previous work we have proposed a data mining model to capture user web navigation patterns, wh... more In previous work we have proposed a data mining model to capture user web navigation patterns, which models the navigation sessions as a hypertext probabilistic grammar. The grammar’s higher probability strings correspond to the user preferred trails and an algorithm was given to find all strings with probability above a threshold. Herein, we propose a heuristic aimed at finding longer trails composed of links whose average probability is above the threshold. A dynamic threshold is provided whose value is at all times proportional to the length of the trail being evaluated. We report on experiments with both real and synthetic data which were conducted to assess the heuristic’s utility.
... 142 5.14 Evolution of the characteristics of the rule-set obtained with the geo-... the form ... more ... 142 5.14 Evolution of the characteristics of the rule-set obtained with the geo-... the form of hyperlinks, see (Chakrabarti et al., 1999a), where links on the web can be viewed as a mechanism of implicit endorsement. Finally, web usage mining focuses ...
We propose a data mining model that captures the user navigation behaviour patterns. The user nav... more We propose a data mining model that captures the user navigation behaviour patterns. The user navigation sessions are modelled as a hypertext probabilistic grammar whose higher probability strings correspond to the user’s preferred trails. An algorithm to efficiently mine such trails is given. We make use of the N gram model which assumes that the last N pages browsed affect the probability of the next page to be visited. The model is based on the theory of probabilistic grammars providing it with a sound theoretical foundation for future enhancements. Moreover, we propose the use of entropy as an estimator of the grammar’s statistical properties. Extensive experiments were conducted and the results show that the algorithm runs in linear time, the grammar’s entropy is a good estimator of the number of mined trails and the real data rules confirm the effectiveness of the model.
One of the main activities of Web users, known as ‘surfing’, is to follow links. Lengthy navigati... more One of the main activities of Web users, known as ‘surfing’, is to follow links. Lengthy navigation often leads to disorientation when users lose track of the context in which they are navigating and are unsure how to proceed in terms of the goal of their original query. Studying navigation patterns of Web users is thus important, since it can lead us to a better understanding of the problems users face when they are surfing. We derive Zipf's rank frequency law (i.e., an inverse power law) from an absorbing Markov chain model of surfers' behavior assuming that less probable navigation trails are, on average, longer than more probable ones. In our model the probability of a trail is interpreted as the relevance (or ‘value’) of the trail. We apply our model to two scenarios: in the first the probability of a user terminating the navigation session is independent of the number of links he has followed so far, and in the second the probability of a user terminating the navigation session increases by a constant each time the user follows a link. We analyze these scenarios using two sets of experimental data sets showing that, although the first scenario is only a rough approximation of surfers' behavior, the data is consistent with the second scenario and can thus provide an explanation of surfers' behavior.
In this work we propose a generalisation of the notion of association rule in the context of flat... more In this work we propose a generalisation of the notion of association rule in the context of flat transactions to that of a composite association rule in the context of a structured directed graph, such as the world-wide-web. The techniques proposed aim at finding patterns in Che user behaviour when traversing such a hypertext system. We redefine the concepts of confidence and support for composite association rules, and two algorithms to mine such rules are proposed. Extensive experiments with random data were conducted and the results show that, in spite of the worst-case complexity analysis which indicates exponential behaviour, in practice the algorithms' complexity, measured in the number of iterations performed, is linear in the number of nodes traversed.
... The HPG model provides a simple and sound tool to summarise the user interaction with the web... more ... The HPG model provides a simple and sound tool to summarise the user interaction with the web and which is potentially useful both to the web site designer and to the individual user. On the one hand, by using a HPG to analyse ...
IEEE Transactions on Knowledge and Data Engineering, 2007
Markov models have been widely used to represent and analyse user web navigation data. In previou... more Markov models have been widely used to represent and analyse user web navigation data. In previous work we have proposed a method to dynamically extend the order of a Markov chain model and a complimentary method for assessing the predictive power of such a variable length Markov chain. Herein, we review these two methods and propose a novel method for measuring the ability of a variable length Markov model to summarise user web navigation sessions up to a given length. While the summarisation ability of a model is important to enable the identification of user navigation patterns, the ability to make predictions is important in order to foresee the next link choice of a user after following a given trail so as, for example, to personalise a web site. We present an extensive experimental evaluation providing strong evidence that prediction accuracy increases linearly with summarisation ability.
In previous work we have proposed a data mining model to capture user web navigation patterns, wh... more In previous work we have proposed a data mining model to capture user web navigation patterns, which models the navigation sessions as a hypertext probabilistic grammar. The grammar’s higher probability strings correspond to the user preferred trails and an algorithm was given to find all strings with probability above a threshold. Herein, we propose a heuristic aimed at finding longer trails composed of links whose average probability is above the threshold. A dynamic threshold is provided whose value is at all times proportional to the length of the trail being evaluated. We report on experiments with both real and synthetic data which were conducted to assess the heuristic’s utility.
... 142 5.14 Evolution of the characteristics of the rule-set obtained with the geo-... the form ... more ... 142 5.14 Evolution of the characteristics of the rule-set obtained with the geo-... the form of hyperlinks, see (Chakrabarti et al., 1999a), where links on the web can be viewed as a mechanism of implicit endorsement. Finally, web usage mining focuses ...
We propose a data mining model that captures the user navigation behaviour patterns. The user nav... more We propose a data mining model that captures the user navigation behaviour patterns. The user navigation sessions are modelled as a hypertext probabilistic grammar whose higher probability strings correspond to the user’s preferred trails. An algorithm to efficiently mine such trails is given. We make use of the N gram model which assumes that the last N pages browsed affect the probability of the next page to be visited. The model is based on the theory of probabilistic grammars providing it with a sound theoretical foundation for future enhancements. Moreover, we propose the use of entropy as an estimator of the grammar’s statistical properties. Extensive experiments were conducted and the results show that the algorithm runs in linear time, the grammar’s entropy is a good estimator of the number of mined trails and the real data rules confirm the effectiveness of the model.
One of the main activities of Web users, known as ‘surfing’, is to follow links. Lengthy navigati... more One of the main activities of Web users, known as ‘surfing’, is to follow links. Lengthy navigation often leads to disorientation when users lose track of the context in which they are navigating and are unsure how to proceed in terms of the goal of their original query. Studying navigation patterns of Web users is thus important, since it can lead us to a better understanding of the problems users face when they are surfing. We derive Zipf's rank frequency law (i.e., an inverse power law) from an absorbing Markov chain model of surfers' behavior assuming that less probable navigation trails are, on average, longer than more probable ones. In our model the probability of a trail is interpreted as the relevance (or ‘value’) of the trail. We apply our model to two scenarios: in the first the probability of a user terminating the navigation session is independent of the number of links he has followed so far, and in the second the probability of a user terminating the navigation session increases by a constant each time the user follows a link. We analyze these scenarios using two sets of experimental data sets showing that, although the first scenario is only a rough approximation of surfers' behavior, the data is consistent with the second scenario and can thus provide an explanation of surfers' behavior.
In this work we propose a generalisation of the notion of association rule in the context of flat... more In this work we propose a generalisation of the notion of association rule in the context of flat transactions to that of a composite association rule in the context of a structured directed graph, such as the world-wide-web. The techniques proposed aim at finding patterns in Che user behaviour when traversing such a hypertext system. We redefine the concepts of confidence and support for composite association rules, and two algorithms to mine such rules are proposed. Extensive experiments with random data were conducted and the results show that, in spite of the worst-case complexity analysis which indicates exponential behaviour, in practice the algorithms' complexity, measured in the number of iterations performed, is linear in the number of nodes traversed.
... The HPG model provides a simple and sound tool to summarise the user interaction with the web... more ... The HPG model provides a simple and sound tool to summarise the user interaction with the web and which is potentially useful both to the web site designer and to the individual user. On the one hand, by using a HPG to analyse ...
IEEE Transactions on Knowledge and Data Engineering, 2007
Markov models have been widely used to represent and analyse user web navigation data. In previou... more Markov models have been widely used to represent and analyse user web navigation data. In previous work we have proposed a method to dynamically extend the order of a Markov chain model and a complimentary method for assessing the predictive power of such a variable length Markov chain. Herein, we review these two methods and propose a novel method for measuring the ability of a variable length Markov model to summarise user web navigation sessions up to a given length. While the summarisation ability of a model is important to enable the identification of user navigation patterns, the ability to make predictions is important in order to foresee the next link choice of a user after following a given trail so as, for example, to personalise a web site. We present an extensive experimental evaluation providing strong evidence that prediction accuracy increases linearly with summarisation ability.
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Papers by José Borges