Abstract—The appearance of a vast array of online newspapers makes appropriate news recommendation for users become critically important. A number of researches in this area have been conducted in recent years. This paper presents the Vietnamese content-based news recommender system. Based on information about news stories that users have read and given feedback, the system can automatically determine news stories that users may want to read later. The center of the system is a hybrid user profile which can diversely model news reading features of individual users thanks to a combination of short-term and long-term information models along with self-description. The paper also introduces and evaluates a mechanism for implicit feedback. After installing the system and conducting several experiments, as well as collecting feedback from users, it is shown that the system is relatively adaptable to users and can recommend appropriate news with a high degree of accuracy, which significantly increases the effectiveness of gathering information compared to traditional news reading.

Index Terms—Implicit feedback, news recommender, user modeling.

The authors are with the Vietnam National University — University of Engineering and Technology, Hanoi, Vietnam.

Cite:Nguyen Thac Huy, Do The Chuan, Viet Anh Nguyen, "Implicit Feedback Mechanism to Manage User Profile Applied in Vietnamese News Recommender System," International Journal of Computer and Communication Engineering vol. 5, no. 4, pp. 276-285, 2016.