User behaviour analysis based on traffic log in wireless networks can be beneficial to many fields in real life: not only for commercial purposes, but also for improving network service quality and social management. We cluster users into groups marked by the most frequently visited websites to find their preferences. In this paper, we propose a user behaviour model based on Topic Model from document classification problems. We use the logarithmic TF-IDF (term frequency - inverse document frequency) weighing to form a high-dimensional sparse feature matrix. Then we apply LSA (Latent semantic analysis) to deduce the latent topic distribution and generate a low-dimensional dense feature matrix. K-means++, which is a classic clustering algorithm, is then applied to the dense feature matrix and several interpretable user clusters are found. Moreover, by combining the clustering results with additional demographical information, including age, gender, and financial information, we are able to uncover more realistic implications from the clustering results.
Insights into the behavior and preference of mobile device users from their web browsing application activities are critical components of any successful dynamic content recommendation system, mobile advertisement platform, or web personalization initiative. In this paper we use an unsupervised topic model to understand the interests of the cellular users based upon their browsing profile. We posit that the length of time a user remains on a given website is positively correlated with the user’s interest in the website’s content. We propose an extended model to integrate this duration information efficiently by oversampling the URLs. This study evaluates methods for clustering web users via vector space models, for the purpose of web page prefetching for possible applications of server optimization. An experiment using Latent S ...