A Novel Method of Articles Rating Based on Concerns Tracking and Matching for Public Opinion Recommendation

Public opinion events on the Internet are gaining more and more attention from the supervisory institutions for the possibility of malicious guide. Since the number of the events on the Internet is quite enormous, the process of supervision often costs a lot of manpower, which is contrary to the purposes and objectives of Sustainable Computing. However, most traditional methods for news recommendation are designed for netizens who do not have specific responsibilities like supervisory institutions. It is also difficult for supervisory institutions themselves to rate the public opinion articles, which is indispensable for recommendation. In this paper, a novel articles rating method based on tracking and matching (ARTM), is proposed for public opinion recommendation. The ARTM method can mine institution concerns from the browsing history and keep them updating automatically with the changing of institution attention. The processing flow of ARTM is as follows. Firstly, a set of institution concerns are established in terms of three aspects: fixed concerns, potential concerns and reading preferences. Then ratings of public opinion articles are computed by measuring the similarities between the vector of article keywords and the vector of institution concerns. Finally, articles are sorted by ratings and high- ranking articles are added to the recommendation list. In addition, the proposed rating algorithm and tracking algorithm can also be used as standalone modules for other services. In the end, comprehensive evaluation of the proposed method based on real data (78 supervisory institutions browsing history in one month) is made. Experimental results show that the proposed ARTM method can significantly improve recommendation efficiency.