Online community platforms require dynamic personalized retrieval and recommendation that can continuously adapt to evolving user interests and new documents. However, optimizing models to handle such changes in real-time remains a major challenge in large-scale industrial settings. To address this, we propose the Interest-aware Representation and Alignment (IRA) framework, an efficient and scalable approach that dynamically adapts to new interactions through a cumulative structure. IRA leverages two key mechanisms: (1) Interest Units that capture diverse user interests as contextual texts, while reinforcing or fading over time through cumulative updates, and (2) a retrieval process that measures the relevance between Interest Units and documents based solely on semantic relationships, eliminating dependence on click signals to mitigate temporal biases. By integrating cumulative Interest Unit updates with the retrieval process, IRA continuously adapts to evolving user preferences, ensuring robust and fine-grained personalization without being constrained by past training distributions. We validate the effectiveness of IRA through extensive experiments on real-world datasets, including its deployment in the Home Section of NAVER's CAFE, South Korea's leading community platform.

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.

Some news headlines mislead readers with overrated or false information, and identifying them in advance will better assist readers in choosing proper news stories to consume. This research introduces million-scale pairs of news headline and body text dataset with incongruity label, which can uniquely be utilized for detecting news stories with misleading headlines. On this dataset, we develop two neural networks with hierarchical architectures that model a complex textual representation of news articles and measure the incongruity between the headline and the body text. We also present a data augmentation method that dramatically reduces the text input size a model handles by independently investigating each paragraph of news stories, which further boosts the performance. Our experiments and qualitative evaluations demonstrate that the proposed methods outperform existing approaches and efficiently detect news stories with misleading headlines in the real world.

Many researchers have been studied visualization techniques to represent relationships between sets. However, most recent studies focused on the scalability of visualizing set relations, rather than on set-typed data itself. Although solving such problems is important, understanding the structural context of the entire data is also essential for analyzing data. We propose NetSet, which combines two techniques to resolve the limitations in representing set relationships. First, we construct a network to provide a structural overview of the set system. Then, we place a matrix layout to visualize intersections among sets. Finally, by combining these two techniques, NetSet enables them to complement each other. The combination gives analysts both the overview and specific views of the data. Furthermore, NetSet provides both flexible exploration of a set system and quantitative analysis of set intersections. We conducted a case study to demonstrate how the combination can be successfully applied to real data, namely topic-talks data from the TED organization.