Modeling Web Navigation using Grammatical Inference

Abstract In this paper, a method that models user navigation on the Web, as opposed to a single Web site, is presented, aiming to assist the user by recommending pages. User modeling is done through data mining of Web usage logs, resulting in aggregate, rather than personal models. The proposed approach extends Grammatical Inference methods, by introducing an extra merging criterion, which examines the semantic similarity of automaton states. The experimental results showed that the method does indeed facilitate the modeling of Web navigation, which was not possible with the existing Web usage mining methods. However, a content-based recommendation model is shown to still outperform the proposed method, which suggests that the knowledge of the navigation sequence does not contribute to the recommendation process. This is due to the thematic cohesion of navigation sessions, in comparison to the large thematic diversity of Web usage data. Among three variants of the proposed method, the one based on Blue Fringe, that examines a larger space of possible merges, performs better

Graph Transformation Based Guidance for Web Navigation

With growing information volume and diverse user preferences on the web, the performance of web information retrieval has become a critical issue. Web navigation is dramatically influenced by the organizations of web contents. Hence, useful navigation guidance can considerably accelerate the information retrieval process. In this paper, web navigation is formulated as a Directed Group Steiner Forest (DGSF) problem in line graph representation of the website. A heuristic algorithm is proposed to tackle the DGSF problem and attain the suboptimal solution in polynomial time. Simulations are conducted to compare the mean searching time for the proposed DGSF-based navigation guidance and other approaches. The results suggest that the DGSF-based navigation guidance can significantly reduce the mean searching time, especially when the number of web pages is large while the number of destination pages is moderate. The discussion is also made for extending the model to take into account the websites owner’s interests and other concerns as well

Comment coproduisons-nous notre environnement numérique marchand ?

International audienceAvec le développement des Technologies de l'Information et de la Communication (TIC) au sein des diverses sphères de la société, nous sommes actuellement conduit à produire une grande quantité de données numériques (i.e. : les big data) durant nos activités quotidiennes. Ces big data constituent autant d'indices que les e-commerçants mobilisent afin de personnaliser automatiquement les environnements numériques de leurs consommateurs. Pour ce faire, ils développent différents systèmes de filtrage destinés à améliorer la qualité de leurs services. C'est pourquoi, afin de mieux comprendre ce processus, nous proposons dans cet article de rendre compte de la manière dont nous coproduisons nos environnements numériques marchands. Nous verrons ainsi que cette coproduction recouvre finalement une asymétrie de conception qui la distingue très nettement d'une cocréation