강성구 연구실

Recommender systems are pivotal in modern digital ecosystems, driving user engagement and satisfaction across various domains such as e-commerce, media streaming, and online education. We focus on addressing key challenges in real-world applications, such as learning user preferences from limited feedback, optimizing accuracy-efficiency trade-offs, and personalizing recommendations across multi-domain and multi-modal contexts.

Information retrieval lies at the heart of how humans interact with vast amounts of digital information. We focus on tackling practical challenges in various domains, including reducing reliance on human annotations, leveraging structured knowledge to enhance relevance prediction, building interactive and conversational systems for improved user engagement.

Data and web mining play a critical role in extracting actionable insights from the vast and ever-growing volumes of unstructured information on the web. These fields enable us to analyze, understand, and utilize patterns in data to power applications from search engines and recommender systems to fraud detection and social network analysis.