Bridging Textual-Collaborative Gap through Semantic Codes for Sequential Recommendation
通过 Q-Former 将 semantic IDs 转换为 textual IDs
2025
TokenFlow: Unified Image Tokenizer for Multimodal Understanding and Generation
兼顾 Low-level 的 Pixel 信息和 High-level 的 Semantic 信息
UniCode$^2$: Cascaded Large-scale Codebooks for Unified Multimodal Understanding and Generation
非常自然的 Image-Codeword+Text-LLM-NextCodeWord-Generation 流程
Dynamic Chunking for End-to-End Hierarchical Sequence Modeling
符号序列的自动切分, 探究非 Subword Tokenizer 的可能性
SoftVQ-VAE: Efficient 1-Dimensional Continuous Tokenizer
TiTok 框架 + Softmax 版 Vector Quantization 以期更高的压缩比
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
OneRec: Unifying Retrieve and Rank with Generative Recommender and Preference Alignment
OneRec, 端到端的推荐模型
Restructuring Vector Quantization with The Rotation Trick
一种利用 Rotation Trick 来替代 STE 的方案
Is Every Item Worth An Embedding?
是否每个 Item 都值得一个可学习的 Embedding 呢