RecBase: Generative Foundation Model Pretraining for Zero-Shot Recommendation
在多个开源数据集上训练的生成式推荐模型, 利用课程学习训练 Tokenizer
Sequential Recommendation
Unified Semantic and ID Representation Learning for Deep Recommenders
混合距离用于量化匹配 & 端到端的联合训练
On the Markovian Nature of the Next-Item Recommendation Task
序列推荐任务的马尔科夫性
Generative Recommendation with Semantic IDs: A Practitioner’s Handbook
分析比较了现有生成式推荐的 Tricks 并给出了一个训练框架
Revisiting Self-attention for Cross-domain Sequential Recommendation
利用多任务/多目标优化学习到更佳的 Attention 分布, 促进跨域推荐
Bridging Textual-Collaborative Gap through Semantic Codes for Sequential Recommendation
通过 Q-Former 将 semantic IDs 转换为 textual IDs
EARN: Efficient Inference Acceleration for LLM-based Generative Recommendation by Register Tokens
通过减小 KV cache size 加速 LLMRec 的推理
Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations
COBRA, 链接离散编码和稠密表示的尝试
Adapting Large Language Models by Integrating Collaborative Semantics for Recommendation
LC-Rec, LLM + RQ-VAE + 丰富的多任务训练
Data Augmentation as Free Lunch: Exploring the Test-Time Augmentation for Sequential Recommendation
TTA, Test-Time Augmentation