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20232026
most citedEAGER-LLM: Enhancing Large Language Models as Recommenders through Exogenous Behavior-Semantic Integration

15 citations · 27 across the 14 of their papers we have counts for

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Showing 2024Show all

5 papers · 1 filter

cs.IR2024

Learning Multi-Aspect Item Palette: A Semantic Tokenization Framework for Generative Recommendation

Qijiong Liu, Jieming Zhu, Zhaocheng Du +3

Traditional recommendation models often rely on unique item identifiers (IDs) to distinguish between items, which can hinder their ability to effectively leverage item content info…

cs.IR2024

EAGER: Two-Stream Generative Recommender with Behavior-Semantic Collaboration

Ye Wang, Jiahao Xun, Minjie Hong +8

Generative retrieval has recently emerged as a promising approach to sequential recommendation, framing candidate item retrieval as an autoregressive sequence generation problem. H…

cs.IR2024

CART: A Generative Cross-Modal Retrieval Framework with Coarse-To-Fine Semantic Modeling

Minghui Fang, Shengpeng Ji, Jialong Zuo +9

Cross-modal retrieval aims to search for instances, which are semantically related to the query through the interaction of different modal data. Traditional solutions utilize a sin…

cs.IR2024★ 4 cited

Multimodal Pretraining, Adaptation, and Generation for Recommendation: A Survey

Qijiong Liu, Jieming Zhu, Yanting Yang +6

Personalized recommendation serves as a ubiquitous channel for users to discover information tailored to their interests. However, traditional recommendation models primarily rely…

cs.CV2024

Enhancing Multimodal Unified Representations for Cross Modal Generalization

Hai Huang, Yan Xia, Shengpeng Ji +7

To enhance the interpretability of multimodal unified representations, many studies have focused on discrete unified representations. These efforts typically start with contrastive…