Aligning Multimodal Sequential Recommendations via Robust Direct Preference Optimization with Sparse MoE

ArXi:2603.29259v1 Announce Type: cross Preference-based alignment objectives have been widely adopted, from RLHF-style pairwise learning in large language models to emerging applications in recommender systems. Yet, existing work rarely examines how Direct Preference Optimization (DPO) behaves under implicit feedback, where unobserved items are not reliable negatives. We conduct systematic experiments on multimodal sequential recommendation to compare common negative-selection strategies and their interaction with DPO.