3 papers
cs.IR2026
Sequences as Nodes for Contrastive Multimodal Graph Recommendation
Bucher Sahyouni, Matthew Vowels, Liqun Chen +1
To tackle cold-start and data sparsity issues in recommender systems, numerous multimodal, sequential, and contrastive techniques have been proposed. While these augmentations can…
cs.IR2026
Multimodal Enhancement of Sequential Recommendation
Bucher Sahyouni, Matthew Vowels, Liqun Chen +1
We propose a novel recommender framework, MuSTRec (Multimodal and Sequential Transformer-based Recommendation), that unifies multimodal and sequential recommendation paradigms. MuS…
cs.LG2026
DSL: Understanding and Improving Softmax Recommender Systems with Competition-Aware Scaling
Bucher Sahyouni, Matthew Vowels, Liqun Chen +1
Softmax Loss (SL) is being increasingly adopted for recommender systems (RS) as it has demonstrated better performance, robustness and fairness. Yet in implicit-feedback, a single…