Research

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. By filtering vast amounts of information and presenting users with personalized suggestions, these systems enhance decision-making, improve user experiences, and significantly impact business revenue. 

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.

Below are examples of recent research projects:

Information retrieval lies at the heart of how humans interact with vast amounts of digital information. From web search engines to domain-specific retrieval systems, IR technologies enable users to efficiently locate relevant information in response to their needs. This capability is essential in today’s data-driven world, powering applications in fields such as e-commerce, healthcare, legal research, and scientific discovery. 

We focus on tackling practical challenges in various domains, where traditional methods often fall short, including reducing reliance on human annotations, leveraging structured knowledge to enhance relevance prediction, building interactive and conversational systems for improved user engagement.

Below are examples of recent research projects:

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 a wide range of applications, from search engines and recommender systems to fraud detection and social network analysis. By transforming raw data into meaningful knowledge, data and web mining contribute significantly to both user-centric and business-driven decision-making. 

The value of data and web mining lies in their ability to uncover hidden structures and relationships within data, enabling systems to make accurate predictions, recommendations, and optimizations. In the context of search and recommendation systems, these techniques facilitate the understanding of user behavior, preferences, and intent, allowing for the development of highly personalized and context-aware experiences. 

Below are examples of recent research projects: