English(EN)Implicit Reasoning for Large Language Model-based Generative Recommendation
新AI模型通过改进的推理和效率增强生成式推荐系统
作者PulseAugur 编辑部·[8 个来源]·
研究人员引入了几种增强生成式推荐系统的新方法，该系统旨在通过将任务构建为序列生成来预测用户偏好。HoloRec提出了一种内源性思维链机制，通过分层语义编码矩阵统一表示、推理和生成。ReaEmb通过将潜在推理增强的对比学习和协同奖励强化学习集成到大语言模型中来解决长尾问题。PauseRec提供了一种轻量级的隐式推理范式，其性能优于显式方法，降低了训练成本并加快了推理速度。此外，VarLenRec学习可变长度分词，解决了项目流行度与区分性语义需求之间的不匹配问题。
AI
arXiv:2606.15331v1 Announce Type: cross Abstract: Generative recommendation models that formulate the task as sequence generation overcome the objective fragmentation problem of traditional cascade architectures, yet existing approaches still suffer from flat semantic representat…
Sequential Recommender Systems (SRS) predict the next item of interest based on users' interaction histories and have been widely deployed, but hindered by long-tail problem. Large Language Models (LLMs), with strong semantic understanding and reasoning capabilities, offer a prom…
arXiv cs.AI
TIER_1English(EN)·Yinhan He, Liam Collins, Bhuvesh Kumar, Jundong Li, Neil Shah, Donald Loveland·
arXiv:2606.14142v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly adopted as backbones for Generative Recommendation (GR), promising access to pretrained world knowledge. Yet reliably invoking this knowledge for GR remains poorly understood. A key ob…
arXiv:2605.17779v2 Announce Type: replace Abstract: Generative recommendation reformulates recommendation as next-token prediction over discrete semantic identifiers (IDs). A fundamental yet unexplored design choice is that existing methods employ fixed-length tokenization for al…
Large Language Models for generative recommendation face challenges with semantic IDs disrupting natural-language reasoning, prompting a lightweight implicit reasoning approach that outperforms explicit methods while reducing computational costs.
Existing sequential recommendation models rely on dataset-specific training, where the learned parameters are fitted to the item catalog and the observed interaction distribution of the training data. This limits generalization to new domains, typically requiring retraining from …
Generative recommendation models that formulate the task as sequence generation overcome the objective fragmentation problem of traditional cascade architectures, yet existing approaches still suffer from flat semantic representations lacking hierarchical structure for multi-step…
Large Language Models (LLMs) are increasingly adopted as backbones for Generative Recommendation (GR), promising access to pretrained world knowledge. Yet reliably invoking this knowledge for GR remains poorly understood. A key obstacle is that LLM-based GR typically represents i…