An Empirical Study of Training ID-Agnostic Multi-modal Sequential Recommenders

doi:arXiv:2403.17372

	An Empirical Study of Training ID-Agnostic Multi-modal Sequential Recommenders
	Li, Youhua1 ; Du, Hanwen 2; Ni, Yongxin 3; He, Yuanqi 4; Fu, Junchen 5; Liu, Xiangyan 3; Guo, Qi 6
	2024-03-30
状态	已发表
摘要	Sequential Recommendation (SR) aims to predict future user -item interactions based on historical interactions. While many SR approaches concentrate on user IDs and item IDs, the human perception of the world through multi -modal signals, like text and images, has inspired researchers to delve into constructing SR from multi -modal information without using IDs. However, the complexity of multi -modal learning manifests in diverse feature extractors, fusion methods, and pre -trained models. Consequently, designing a simple and universal Multi -Modal Sequential Recommendation (MMSR) framework remains a formidable challenge. We systematically summarize the existing multi -modal related SR methods and distill the essence into four core components: visual encoder, text encoder, multimodal fusion module, and sequential architecture. Along these dimensions, we dissect the model designs, and answer the following sub -questions: First, we explore how to construct MMSR from scratch, ensuring its performance either on par with or exceeds existing SR methods without complex techniques. Second, we examine if MMSR can benefit from existing multi -modal pre -training paradigms. Third, we assess MMSR’s capability in tackling common challenges like cold start and domain transferring. Our experiment results across four real -world recommendation scenarios demonstrate the great potential ID -agnostic multi -modal sequential recommendation.
关键词	Multi-modality Sequential Recommendation Transfer Learning
语种	英语
DOI	arXiv:2403.17372
相关网址	查看原文
出处	Arxiv
收录类别	PPRN.PPRN
WOS记录号	PPRN:88296256
WOS类目	Computer Science, Information Systems
文献类型	预印本
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/372940
专题	信息科学与技术学院_硕士生
作者单位	1.ShanghaiTech Univ, Shanghai, Peoples R China 2.Soochow Univ, Suzhou, Peoples R China 3.Natl Univ Singapore, Singapore, Singapore 4.Shanghai Jiaotong Univ, Shanghai, Peoples R China 5.Univ Glasgow, Glasgow, Scotland 6.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Li, Youhua,Du, Hanwen,Ni, Yongxin,et al. An Empirical Study of Training ID-Agnostic Multi-modal Sequential Recommenders. 2024.