Cover Image

Learning ID-free Item Representation with Token Crossing for Multimodal Recommendation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhang, Kangning, Jin, Jiarui, Qin, Yingjie, Su, Ruilong, Lin, Jianghao, Yu, Yong, Zhang, Weinan
Format:	Preprint
Published:	2024
Subjects:	Information Retrieval
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

DREAM: A Dual Representation Learning Model for Multimodal Recommendation
by: Zhang, Kangning, et al.
Published: (2024)

AlignRec: Aligning and Training in Multimodal Recommendations
by: Liu, Yifan, et al.
Published: (2024)

Unleashing the Potential of Multi-Channel Fusion in Retrieval for Personalized Recommendations
by: Huang, Junjie, et al.
Published: (2024)

Why Not Together? A Multiple-Round Recommender System for Queries and Items
by: Jin, Jiarui, et al.
Published: (2024)

Beyond Graph Convolution: Multimodal Recommendation with Topology-aware MLPs
by: Huang, Junjie, et al.
Published: (2024)

A Comprehensive Survey on Retrieval Methods in Recommender Systems
by: Huang, Junjie, et al.
Published: (2024)

M-scan: A Multi-Scenario Causal-driven Adaptive Network for Recommendation
by: Zhu, Jiachen, et al.
Published: (2024)

Generative Representational Learning of Foundation Models for Recommendation
by: Zhou, Zheli, et al.
Published: (2025)

D2K: Turning Historical Data into Retrievable Knowledge for Recommender Systems
by: Qin, Jiarui, et al.
Published: (2024)

An Automatic Graph Construction Framework based on Large Language Models for Recommendation
by: Shan, Rong, et al.
Published: (2024)

Modular Representation Compression: Adapting LLMs for Efficient and Effective Recommendations
by: Xi, Yunjia, et al.
Published: (2026)

Diffusion Model for Interest Refinement in Multi-Interest Recommendation
by: Le, Yankun, et al.
Published: (2025)

MemoCRS: Memory-enhanced Sequential Conversational Recommender Systems with Large Language Models
by: Xi, Yunjia, et al.
Published: (2024)

Learnable Item Tokenization for Generative Recommendation
by: Wang, Wenjie, et al.
Published: (2024)

Learning Item Representations Directly from Multimodal Features for Effective Recommendation
by: Zhou, Xin, et al.
Published: (2025)

Play to Your Strengths: Collaborative Intelligence of Conventional Recommender Models and Large Language Models
by: Xi, Yunjia, et al.
Published: (2024)

Towards Efficient and Effective Unlearning of Large Language Models for Recommendation
by: Wang, Hangyu, et al.
Published: (2024)

Large Language Models Make Sample-Efficient Recommender Systems
by: Lin, Jianghao, et al.
Published: (2024)

A Survey on Diffusion Models for Recommender Systems
by: Lin, Jianghao, et al.
Published: (2024)

ELCoRec: Enhance Language Understanding with Co-Propagation of Numerical and Categorical Features for Recommendation
by: Chen, Jizheng, et al.
Published: (2024)

Learning Partially Aligned Item Representation for Cross-Domain Sequential Recommendation
by: Yin, Mingjia, et al.
Published: (2024)

FLIP: Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR Prediction
by: Wang, Hangyu, et al.
Published: (2023)

Efficient Item ID Generation for Large-Scale LLM-based Recommendation
by: Subbiah, Anushya, et al.
Published: (2025)

Sell It Before You Make It: Revolutionizing E-Commerce with Personalized AI-Generated Items
by: Lin, Jianghao, et al.
Published: (2025)

Beyong Tokens: Item-aware Attention for LLM-based Recommendation
by: Zhang, Xiaokun, et al.
Published: (2026)

Efficiency Unleashed: Inference Acceleration for LLM-based Recommender Systems with Speculative Decoding
by: Xi, Yunjia, et al.
Published: (2024)

Full-Stack Optimized Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation
by: Shan, Rong, et al.
Published: (2025)

How Reliable Are Semantic-ID Tokenizer Comparisons in Generative Recommendation?
by: Zhang, Qian, et al.
Published: (2026)

Learning Structure and Knowledge Aware Representation with Large Language Models for Concept Recommendation
by: Li, Qingyao, et al.
Published: (2024)

ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Prediction
by: Lin, Jianghao, et al.
Published: (2023)

Unified Semantic and ID Representation Learning for Deep Recommenders
by: Lin, Guanyu, et al.
Published: (2025)

PIT: A Dynamic Personalized Item Tokenizer for End-to-End Generative Recommendation
by: Wang, Huanjie, et al.
Published: (2026)

Token-Efficient Item Representation via Images for LLM Recommender Systems
by: Kim, Kibum, et al.
Published: (2025)

Lifelong Personalized Low-Rank Adaptation of Large Language Models for Recommendation
by: Zhu, Jiachen, et al.
Published: (2024)

Generative Recommender with End-to-End Learnable Item Tokenization
by: Liu, Enze, et al.
Published: (2024)

Universal Item Tokenization for Transferable Generative Recommendation
by: Zheng, Bowen, et al.
Published: (2025)

Efficient and Deployable Knowledge Infusion for Open-World Recommendations via Large Language Models
by: Xi, Yunjia, et al.
Published: (2024)

InfoRank: Unbiased Learning-to-Rank via Conditional Mutual Information Minimization
by: Jin, Jiarui, et al.
Published: (2024)

From ID-based to ID-free: Rethinking ID Effectiveness in Multimodal Collaborative Filtering Recommendation
by: Li, Guohao, et al.
Published: (2025)

Tokenize Once, Recommend Anywhere: Unified Item Tokenization for Multi-domain LLM-based Recommendation
by: Hou, Yu, et al.
Published: (2025)