5 papers
Learning Sparse Representations of Multimodal Content for Enhanced Cold Item Recommendation
Gregor Meehan, Johan Pauwels
The scale and rapid growth of item catalogs in modern digital platforms present significant challenges to recommender system (RS) practitioners. Most RSs use embedding similarity t…
Sparse Contrastive Learning for Content-Based Cold Item Recommendation
Gregor Meehan, Johan Pauwels
Item cold-start is a pervasive challenge for collaborative filtering (CF) recommender systems. Existing methods often train cold-start models by mapping auxiliary item content, suc…
On Inherited Popularity Bias in Cold-Start Item Recommendation
Gregor Meehan, Johan Pauwels
Collaborative filtering (CF) recommender systems struggle with making predictions on unseen, or 'cold', items. Systems designed to address this challenge are often trained with sup…
Towards a Unified Representation Evaluation Framework Beyond Downstream Tasks
Christos Plachouras, Julien Guinot, George Fazekas +3
Downstream probing has been the dominant method for evaluating model representations, an important process given the increasing prominence of self-supervised learning and foundatio…
Learning Music Audio Representations With Limited Data
Christos Plachouras, Emmanouil Benetos, Johan Pauwels
Large deep-learning models for music, including those focused on learning general-purpose music audio representations, are often assumed to require substantial training data to ach…