English(EN)Towards Reliable, Scalable, and Value-Aligned Generative Recommendation for Large-Scale Advertising
新研究利用LLM增强生成式推荐系统 · 追踪8个来源
作者PulseAugur 编辑部·[9 个来源]·
研究人员正在开发新的方法来增强生成式推荐系统，该系统使用大型语言模型（LLMs）来生成物品标识符。几篇论文解决了优化语义分词（OptST）、弥合语义量化差距（BRIDGE）和提高解码效率（ZeroGR）等挑战。其他工作则侧重于意图感知优化（IntentGEO）和大规模广告的价值对齐（GR4AD-X），同时也探索如何更好地表示物品语义（ComeIR）和优化搜索策略（Tiger）。这些进展旨在提高推荐质量、效率和商业价值。
AI
Semantic information can enrich the identifiers and structured fields used by recommender systems. Semantic tokenization provides a structured representation of this information by converting LLM-derived user profiles into discrete tokens. In the common two-stage pipeline, the to…
Generative recommendation retrieves items by autoregressively generating Semantic IDs(SIDs), where an RQ-VAE quantizes each item into a hierarchical SID sequence that the decoder generates level by level. We identify an overlooked bottleneck in this paradigm: the Semantic Quantiz…
Generative recommendation is gaining traction in production, retrieving items by generating their identifiers one token at a time. The generated sequences must identify relevant catalog items, so constrained decoding couples relevance scoring with validity filtering. Because most…
Large language models (LLMs) increasingly shape recommendations across chatbots, e-commerce, and user-generated content (UGC) platforms, creating demand for Generative Engine Optimization (GEO) to improve item visibility in generated responses. Existing GEO studies are mainly con…
Large language models (LLMs) increasingly shape recommendations across chatbots, e-commerce, and user-generated content (UGC) platforms, creating demand for Generative Engine Optimization (GEO) to improve item visibility in generated responses. Existing GEO studies are mainly con…
Generative recommendation can reproduce a wider beam's ranked output with roughly half the prefix-capacity budget by reallocating retention capacity across semantic-ID positions. We identify a \emph{first-step coverage bottleneck}: across six full-test settings of the TIGER gener…
arXiv:2605.11447v2 Announce Type: replace-cross Abstract: Generative recommendation (GR) has emerged as a promising paradigm that predicts target items by autoregressively generating their semantic identifiers (SID). Most GR methods follow a quantization-representation-generation…
Generative recommendation (GR) reformulates item retrieval as autoregressive generation of discrete semantic identifiers (SIDs), offering a unified paradigm for personalized recommendation. GR4AD, as an industrial generative recommendation framework, has greatly improved advertis…
