The Cedar team is excited to announce that we will be hosting Manel Slokom, a postdoctoral researcher in the Human-Centered Data Analytics group at CWI, for an upcoming edition of our team seminar series. Her talk will take place on Wednesday, the 20th, at 11 AM in the Thomas Flowers Room and online here.
Title
How to diversify any personalised recommenders?
Abstract
In this work, we introduce a novel approach to improve the diversity of Top-N recommendations while maintaining recommendation performance. Our approach employs a user-centric pre-processing strategy aimed at exposing users to a wide array of content categories and topics. We personalize this strategy by selectively adding and removing a percentage of interactions from user profiles. This personalization ensures we remain closely aligned with user preferences while gradually introducing distribution shifts. Our preprocessing technique offers flexibility and can seamlessly integrate into any recommender architecture. We run extensive experiments on two publicly available data sets for news and book recommendations to evaluate our approach. We test various standard and neural network-based recommender system algorithms. Our results show that our approach generates diverse recommendations, ensuring users are exposed to a wider range of items. Furthermore, leveraging pre-processed data for training leads to recommender systems achieving performance levels comparable to, and in some cases, better than those trained on original, unmodified data. Additionally, our approach promotes provider fairness by facilitating exposure to minority or niche categories.
Bio
Manel Slokom holds a PhD in Computer Science from Delft University of Technology (Link to the thesis). She is currently a postdoctoral researcher in the Human-Centered Data Analytics group at CWI. Her interest in responsible recommender systems began during her PhD, where she focused on purpose-aware, privacy-preserving data for these systems. Manel is also a member of the AI, Media, and Democracy Lab, where she explores issues such as bias, fairness, diversity, and reliable evaluation in (news) recommender systems. In addition to her work with CWI and the AI, Media, and Democracy Lab, Manel held a part-time position at Statistics Netherlands until June 2024. There, she investigated various use cases for synthetic data generation, including data release, education, machine learning, and statistical disclosure control.
