I am a researcher in the field of information retrieval, interested in machine learning, ranking models, recommender systems, and natural language processing. Currently I work as a PhD under the supervision of Claudia Hauff at TU Delft. I had the opportunity of doing research at Amazon and Spotify during research internships. Other than science I am passionate about photography and I am working on my first photobook 🖼️.
Representation learning for rankingText encoders learn representations for queries and documents, which are then used to calculate a relevance score. The goal is that relevant documents get close to the query and non-relevant documents get far from the query in the embedding space. I am interested in many aspects of representation learning, including negative sampling, disentanglement and interpretability.
Explainability and model understandingInformation filtering systems, such as document rankers and recommender systems, have a large impact into what we are able to find, what we are exposed to and the decisions we make. Understanding the behavior of such models, when they fail, how robust they are, and why they are recommending certain items over others is crucial for both machine learning practitioners and end users.
- CHIIR short paperPairwise Review-Based Explanations for Voice Product Search
- ECIR 🏆 best paperEvaluating the Robustness of Retrieval Pipelines with Query Variation Generators
- RecSysWhat does BERT know about books, movies and music? Probing BERT for Conversational Recommendation
- RecSys 🏆 best paper RUExploiting Performance Estimates for Augmenting Recommendation Ensembles
presentationsSlides for our ECIR 2022 paper on query variations. video 🎬.
Slides for the Glasgow IR seminar on 10 May 2021: video 🎬.