|关键词: 新闻 推荐算法 可信评价体系 公平性 可解释性 抗抵赖性
|Trust Evaluation of News Recommendation Algorithms
|LIU Zongzhen,ZHANG Xiaodan,GUO Tao,GE Jingguo,ZHOU Xi,WANG Yuhang,CHEN Jiadi,LV Honglei,LIN Junyu
|School of Cyber Security, University of Chinese Academy of Science, Beijing 100049, China;Institute of Information Engineering, Chinese Academy of Science, Beijing 100093, China
|With the rapid development of new technologies such as AI, 5G, and AR/VR, the applications on content, such as e-commerce, social networks, and short videos et al. have emerged one after another, leading the increasingly serious problem of information overload. The development of artificial intelligence technology has promoted the explosive application of intelligent algorithms. As a kind of intelligent algorithm, driven by big data, application scenarios and computing capability, recommendation algorithms provide the users with personalized and high-quality recommendation services that adapt to their interests and behaviors, which has not only gradually improved the user experience and the efficiency of content distribution, but also alleviated the problem of information overload to a certain extent. However, the potential biases, black-box characteristics and the content distribution methods of recommendation algorithms have gradually brought security challenges such as unfairness and inexplicability on the decision-making results, information cocoon and the infringement of user privacy et al. How to improve the interpretability, fairness, and trust of recommendation algorithms has been paid more and more attention from the regulatory agencies of governments, industries and academia at home and abroad. Therefore, the recommendation systems and recommendation algorithms enter the regulatory period from the development period. To this end, in the news field, by analyzing the key elements of the recommendation algorithm, such as manuscript portraits, user portraits, recommendation, feedback and interventions, and manual reviews, focusing on the participants of the recommendation algorithm ecology, such as the content producers, the audiences, the algorithm models and the news platform, this study proposes a trust evaluation system for the news recommendation algorithms based on fairness, interpretability and anti-denying. At last, we carry out the qualitative or quantitative analysis. Fairness, interpretability, and anti-denying are positively correlated. When the fairness and anti-denying are stronger and the interpretability is higher, the trust of the news recommendation algorithm is higher. It is expected to fill the research gaps in the study of the trust of news recommendation algorithms, establish a trust recommendation algorithm ecology, accelerate the establishment and promotion of secure recommendation systems, provide a reference for research on the trust of intelligent algorithms, and provide better ideas for the supervision and governance of smart algorithms.
|Key words: news recommendation algorithm trust evaluation system fairness interpretability anti-denying