Online news providers such as Google News, Bing News, and NAVER News collect a large number of news articles from a variety of presses and distribute these articles to users via their portals. Dynamic nature of a news domain causes the problem of information overload that makes it difficult for a user to find her preferable news articles. Motivated by this situation, NAVER Corp., the largest portal company in South Korea, identified four design considerations (DCs) for news recommendation that reflect the unique characteristics of a news domain. In this paper, we introduce a large-scale news recommender system named as AiRS, present how it jointly leverages the four DCs for NAVER News service. Specifically, AiRS first generates candidate articles for recommendation to a target user based on collaborative filtering (CF), quality estimation (QE), and social impact (SI) models; then, it ranks the candidate articles based on the scores computed by considering their multi-type feature scores (e.g., user's section preference and article's recency), finally recommending the top-k news articles that a target user is likely to prefer. Also, we present how to build the architecture for online deployment of AiRS at NAVER News. Through extensive offline and online A/B tests using the real-world datasets, we validate that AiRS successfully reflects all of the DCs into the news recommendation process, all design choices employed in AiRS help improve the recommendation accuracy, and AiRS significantly outperforms five state-of-the-art news recommendation approaches in terms of accuracy.