4 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…
Leveraging Artist Catalogs for Cold-Start Music Recommendation
Yan-Martin Tamm, Gregor Meehan, VojtÄch Nekl +4
The item cold-start problem poses a fundamental challenge for music recommendation: newly added tracks lack the interaction history that collaborative filtering (CF) requires. Exis…
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…