Public defence: Roza Abolghasemi
Roza Abolghasemi defended her thesis “Group Recommendation Systems with Pairwise Preference Data” for the PhD programme in Engineering Science.
Roza Abolghasemi defended her thesis “Group Recommendation Systems with Pairwise Preference Data” for the PhD programme in Engineering Science.
Trial lecture: “Generative AI for Recommendation Systems”.
The dissertation is available in ODA Open digital Archive (nva.sikt.no).
Ordinary opponents:
Leader of the public defense: Professor André Brodtkorb, Head of Department of Computer Science OsloMet
Supervisors:
Group recommendation systems (GRS) are designed to find what a group of people likes and suggest things they will enjoy together, such as choosing a movie, deciding where to dine, or planning a trip. These systems aim to find options that suit everyone's preferences, making group activities more enjoyable and harmonious. However, combining the diverse tastes of group members into a single recommendation is a challenging task.
This research focuses on improving GRS by developing new methods to make recommendations both fair and precise. It explores how groups make decisions and uses advanced algorithms to predict what people with varying preferences will enjoy together. To tackle these challenges, the study introduces innovative ways to address key issues, such as dealing with limited preference data, grouping users based on shared interests, and ensuring that everyone’s voice is fairly represented in the final recommendation.
Key contributions of this work include:
By combining these approaches, this research makes significant strides toward creating smarter, fairer, and more accurate group recommendation systems. These advancements pave the way for better tools to enhance social experiences, making group decisions easier and more enjoyable.