Personalized Popular Blog Recommender Service for Mobile Applications

Abstract

Weblogs have emerged as a new communication and publication medium on the Internet for diffusing the latest useful information. Providing value-added mobile services such as blog articles is increasingly important to attract mobile users to mobile commerce. There are, however, a tremendous number of blog articles, and mobile users generally have difficulty in browsing weblogs. Accordingly, providing mobile users with blog articles that suit their interests is an important issue. Very little research, however, focuses on this issue. In this work, we propose a Customized Content Service on a mobile device (m-CCS) to filter and push blog articles to mobile users. The m-CCS can predict the latest popular blog topics by forecasting the trend of time-sensitive popularity of weblogs. Furthermore, to meet the diversified interest of mobile users, m-CCS further analyzes users' browsing logs to derive their interests, which are then used to recommend their preferred popular blog topics and articles. The prototype system of m-CCS demonstrates that the system can effectively recommend mobile users desirable blog articles with respect to both Popularity and personal interests.