arXiv AI
TAAL: Mitigating Early Beam Pruning in Generative Recommendation via Temporal Autoregressive Alignment
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EchoRec: Multi-Item Prediction-Empowered Generative Recommendation via Cycle-Consistent Preference Alignment
arXiv:2608. 14011v1 Announce Type: cross Abstract: Generative recommendation autoregressively generates the semantic IDs of the target item, unifying preference modeling and index retrieval within the shared token space.
arXiv Machine Learning
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Guiding the coarse levels of semantic IDs makes the fine levels learnable
arXiv:2609.22227v1 Announce Type: cross Abstract: Generative retrieval represents each item by a short Semantic ID and casts recommendation as autoregressive generation of that sequence. Because the...
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Understanding Semantic IDs: From Item Representation to Item Selection in Generative Recommendation
arXiv:2607. 24995v1 Announce Type: new Abstract: Semantic IDs (SIDs) are now a central component of generative recommendation.
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Empowering Cross-Domain Sequential Recommendation with Hybrid Tokenization and Serial-Parallel Decoding
arXiv:2607. 28659v1 Announce Type: new Abstract: Cross-domain sequential recommendation (CDSR) aims to model users' dynamic interest transitions and sequential patterns across multiple domains.
arXiv AI
Aug 24
Difficulty-Aware Semantic-ID Optimization for Generative Recommendation
The paper introduces Difficulty‑Aware Semantic‑ID Optimization (DASO), a post‑training method for generative recommendation that improves tree‑structured item ranking. DASO profiles rollout groups by prefix‑match depth, reallocates a portion of candidates to prefix‑guided completions, and uses a SID‑prefix reward with an auxiliary SFT anchor to address target‑missing failures. On public benchmarks, DASO outperforms MiniOneRec‑style GRPO on 11 of 12 metrics and achieves the best results on 9 of 12 metrics, also improving level‑wise recall on an internal recommendation task.
Hugging Face Trending Papers
Sep 24
From Interests to Semantic IDs: Retrieval-Grounded Credit Assignment for Generative Recommendation
The paper introduces a retrieval‑grounded credit‑assignment method for generative recommenders that use Semantic IDs (SIDs). By structuring each autoregressive trace into a history summary, a set of interest hypotheses, and a final SID, a frozen retriever verifies each hypothesis as a catalog query. Rewards are assigned at the hypothesis level when any query retrieves the target within the top‑K, allowing distinct updates for rollouts that share the same SID reward and improving SID recommendation performance on Amazon Reviews datasets.