Sep 2026· Proceedings of the 20th ACM Conference on Recommender Systems· pp. 675-679· 0 citations· 19 references
Abstract
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 mismatch limits semantic transfer from the tokenizer to the recommender and may hurt overall optimization. We tackle this limitation by introducing a distribution-based supervision scheme for generative recommendation, where multi-level codebook probabilities are treated as soft semantic targets. On top of this design, we develop 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. The proposed method enriches training with finer semantic cues while leaving the decoding stage unchanged. Experimental studies on multiple real-world benchmarks demonstrate that SODA consistently strengthens diverse generative recommendation architectures. Code is available at https://github.com/freyasa/SODA
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
PALRec is proposed, a parameter-preserving augmentation framework that equips an LLM with recommendation capabilities while keeping its original parameters fixed and consistently outperforms fully fine-tuned counterparts in recommendation accuracy while preserving the LLM’s pre-trained knowledge.
Hyunsoo Na, Minseok Gang, Sang-goo Lee et al.· ACM Transactions on Informat...· 0 citations
Generative recommendation reformulates sequential recommendation as autoregressive generation by encoding items into semantic tokens, enabling improved scaling capability and cross-domain generalization. However, existing generative recommender systems typically follow a two-stage pipeline, where item tokenization is l...
Jia-Yi Dan, Wei-Jian Li, Yong-Qi Liu et al.· Proceedings of the 20th ACM...· 0 citations
This work proposes a simple, parameter-free intervention that initializes SID token embeddings directly from their corresponding centroids in the semantic embedding space, and shows that preserving SID geometry, beyond shared-prefix structure, provides a simple and effective semantic prior for LLM-based GR.
Donald Loveland, Liam Collins, B. Kumar et al.· 0 citations
This research provides deep insights into the capabilities of encoder models, offering a unique, controlled benchmark of over 20 state-of-the-art models. The aim is to analyze the viability of a potentially advantageous recommender architecture that capitalizes on the progress made in natural language processing (NLP)...
Patrik Müller, Bogdan Walek, R. Farana· Computers· 0 citations
Recent advances in generative modeling have reshaped recommender systems by formulating recommendation as a next-item generation problem. Existing retrieval approaches primarily follow two paradigms: user-to-item (U2I) methods represent user context using one or a few deterministic embeddings, which limits the ability...
Cheng-Lei Shen, Chen-Zhe Huang, Dong Jiang et al.· 0 citations
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