arXiv Machine Learning August 3, 2026 By Huanyu Liu, Baining Chen, Hui Liu, Zengyang Li, Ziyi Huang

PaletteID: Prototype-Composed Semantic Identifiers for Multimodal CTR Prediction

Read the original on arXiv Machine Learning →

arXiv:2607. 29000v1 Announce Type: cross Abstract: Multimodal information can improve the accuracy of click-through rate (CTR) prediction and effectively alleviate item cold-start and long-tail problems.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI

Jul 29

VaLiDRec: Variable-Length LLM-Aligned Semantic IDs for Generative Recommendation

arXiv:2607. 25209v1 Announce Type: cross Abstract: Generative recommendation commonly represents items using fixed-length semantic identifiers (SIDs) constructed through clustering and quantization.

By Shutong Qiao, Wei Yuan, Tong Chen, Hao Wang, Quoc Viet Hung Nguyen, Hongzhi Yin

llms efficiency

arXiv Machine Learning

Jun 15

PCR-CA: Parallel Codebook Representations with Contrastive Alignment for Multiple-Category App Recommendation

arXiv:2508. 18166v5 Announce Type: replace-cross Abstract: Modern app store recommender systems struggle with multiple-category apps, as traditional taxonomies fail to capture overlapping semantics, leading to suboptimal personalization.

By Bin Tan, Wangyao Ge, Yidi Wang, Xin Liu, Jeff Burtoft, Hao Fan, Hui Wang

rag efficiency multimodal safety

arXiv Machine Learning

Jun 25

TokenMinds: Pretrained User Tokens and Embeddings for User Understanding in Large Recommender Systems

arXiv:2606. 25147v1 Announce Type: cross Abstract: User modeling in industrial recommender systems typically produces dense embeddings, which suffer from representational constraints inherent to fixed-dimensional vectors.

By Qingyun Liu, Bo Yan, Yang Liu, Yuji Roh, Ekansh Sharma, Likang Yin, Emma Olowo, Min-hsuan Tsai, Yuxuan Li, Diego Uribe, Saksham Aggarwal, Siqi Wu, Yuan Hao, Vikas Kedigehalli, Lukasz Heldt, Lichan Hong, Li Wei, Xinyang Yi

llms rag nlp

arXiv AI

Jun 2

Deep Interest Mining for Intent-Enriched Semantic IDs in Multimodal Generative Recommendation

arXiv:2604. 20861v3 Announce Type: replace-cross Abstract: Semantic IDs (SIDs) provide the discrete item vocabulary used by generative recommendation, but their quality depends on what item evidence is preserved before quantization.

By Yangchen Zeng, Jinze Wang

llms rag reinforcement-learning efficiency multimodal

arXiv AI

Jul 29

TopoGR: Revealing and Preserving Latent Structure of Semantic ID in Generative Recommendation

arXiv:2607. 25216v1 Announce Type: cross Abstract: Semantic ID-based generative recommendation tokenizes each item into a sequence of discrete semantic IDs and predicts the next item by generating semantic IDs.

By Ziyu Zheng, Zhengshun Du, Yaming Yang, Bin Tong, Guan Wang, Meng Yan, Ziyu Guan, Wei Zhao

rag nlp benchmarks

arXiv AI

Sep 1

CoFiRec: Coarse-to-Fine Tokenization for Generative Recommendation

arXiv:2511.22707v2 Announce Type: replace-cross Abstract: In web environments, user preferences are often refined progressively as users move from browsing broad categories to exploring specific item...

By Tianxin Wei, Xuying Ning, Xuxing Chen, Ruizhong Qiu, Yupeng Hou, Yan Xie, Shuang Yang, Zhigang Hua, Jingrui He

llms rag diffusion nlp efficiency benchmarks

One email a morning, machine-written

Related stories

VaLiDRec: Variable-Length LLM-Aligned Semantic IDs for Generative Recommendation

PCR-CA: Parallel Codebook Representations with Contrastive Alignment for Multiple-Category App Recommendation

TokenMinds: Pretrained User Tokens and Embeddings for User Understanding in Large Recommender Systems

Deep Interest Mining for Intent-Enriched Semantic IDs in Multimodal Generative Recommendation

TopoGR: Revealing and Preserving Latent Structure of Semantic ID in Generative Recommendation

CoFiRec: Coarse-to-Fine Tokenization for Generative Recommendation