Abstract. Web transaction data between web visitors and web functionalities usually convey users ’ task-oriented behavior patterns. Clustering web transactions, thus, may capture such informative knowledge, in turn, build user profiles, which are associated with different navigational patterns. For some advanced web applications, such as web recommendation or personalization, the aforementioned work is crucial to make web users get their preferred information accurately. On the other hand, the conventional web usage mining techniques for clustering web objects often perform clustering on usage data directly rather than take the underlying semantic relationships among the web objects into account. Latent Semantic Analysis (LSA) model is a commonly used approach for capturing semantic associations among co-occurrence observations.. In this paper, we propose a LSA-based approach for such purpose. We demonstrated usability and scalability of the proposed approach through performing experiments on two real world datasets. The experimental results have validated the method’s effectiveness in comparison with some previous studies. 1

The locality of web pages within a web site is initially determined by the designer’s expectation. Web usage mining can discover the patterns in the navigational behaviour of web visitors, in turn, improve web site functionality and service designing by considering users ’ actual opinion. Conventional web page clustering technique is often utilized to reveal the functional similarity of web pages. However, high-dimensional computation problem will be incurred due to taking user transaction as dimension. In this paper, we propose a new web page grouping approach based on Probabilistic Latent Semantic Analysis (PLSA) model. An iterative algorithm based on maximum likelihood principle is employed to overcome the aforementioned computational shortcoming. The web pages are classified into various groups according to user access patterns. Meanwhile, the semantic latent factors or tasks are characterized by extracting the content of “dominant ” pages related to the factors. We demonstrate the effectiveness of our approach by conducting experiments on real world data sets. 1.

Web transaction data usually convey user task-oriented behaviour pattern. Web usage mining technique is able to capture such informative knowledge about user task pattern from usage data. With the discovered usage pattern information, it is possible to recommend Web user more preferred content or customized presentation according to the derived task preference. In this paper, we propose a Web recommendation framework based on discovering task-oriented usage pattern with Probabilistic Latent Semantic Analysis (PLSA) model. The user intended tasks are characterized by the latent factors through probabilistic inference, to represent the user navigational interests. Moreover, the active user's intuitive task-oriented preference is quantized by the probabilities, by which pages visited in current user session are associated with various tasks as well. Combining the identified task preference of current user with the discovered usage-based Web page categories, we can present user more potentially interested or preferred Web content. The preliminary experiments performed on real world data sets demonstrate the usability and effectiveness of the proposed approach.