A fine grained heuristic to capture web navigation patterns

In previous work we have proposed a statistical model to capture the user behaviour when browsing the web. The user navigation information obtained from web logs is modelled as a hypertext probabilistic grammar (HPG) which is within the class of regular probabilistic grammars. The set of highest probability strings generated by the grammar corresponds to the user preferred navigation trails. We have previously conducted experiments with a Breadth-First Search algorithm (BFS) to perform the exhaustive computation of all the strings with probability above a specified cut-point, which we call the rules. Although the algorithm’s running time varies linearly with the number of grammar states, it has the drawbacks of returning a large number of rules when the cut-point is small and a small set of very short rules when the cut-point is high. In this work, we present a new heuristic that implements an iterative deepening search wherein the set of rules is incrementally augmented by first exploring trails with high probability. A stopping parameter is provided which measures the distance between the current rule-set and its corresponding maximal set obtained by the BFS algorithm. When the stopping parameter takes the value zero the heuristic corresponds to the BFS algorithm and as the parameter takes values closer to one the number of rules obtained decreases accordingly. Experiments were conducted with both real and synthetic data and the results show that for a given cut-point the number of rules induced increases smoothly with the decrease of the stopping criterion. Therefore, by setting the value of the stopping criterion the analyst can determine the number and quality of rules to be induced; the quality of a rule is measured by both its length and probability

Report of the First International Workshop on Learning over Multiple Contexts (LMCE 2014)

© ACM 2015. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM SIGKDD Explorations Newsletter http://dx.doi.org/10.1145/2830544.2830551The first international workshop on Learning over Multiple Contexts, devoted to generalization and reuse of machine learning models over multiple contexts, was held on September 19th, 2014, as part of the 7th European machine learning and data mining conference (ECML-PKDD 2014) in Nancy, France. This short report summarizes the presentations and discussions held during the LMCE 2014 workshop, as well as the workshop conclusions and the future agenda.Ferri Ramírez, C.; Flach, P.; Lachiche, N. (2015). Report of the First International Workshop on Learning over Multiple Contexts (LMCE 2014). ACM SIGKDD Explorations Newsletter. 17(1):48-50. doi:10.1145/2830544.2830551S485017