SODA, a plug-and-play alignment framework that adopts a BPR-style contrastive objective to align recommender representations with target-side distributional representations against negative ones, is developed and demonstrated that SODA consistently strengthens diverse generative recommendation architectures.
Zi-Qiu Xue, Ding-Xian Wang, Yi-Meng Bai et al.· Proceedings of the 20th ACM...· 0 citations
Recent generative recommenders improve scalability by retrieving items through token generation instead of traditional ranking over large candidate sets. Yet their training signals are still dominated by discrete code prediction, which overlooks the soft assignment information naturally produced by the tokenizer. This...
Zi-Qiu Xue, Ding-Xian Wang, Yi-Meng Bai et al.· Proceedings of the 20th ACM...· 0 citations
Experiments on the LongLaMP dataset show that PrefReward outperforms non-personalized and retrieval-based baselines in both generation quality and personalization interpretability.
Yue Wu, Chengbing Wang, Yimeng Bai et al.· arXiv.org· 0 citations
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