How can recommender systems benefit from large language models: A survey
With the rapid development of online services, recommender systems (RS) have become
increasingly indispensable for mitigating information overload. Despite remarkable …
increasingly indispensable for mitigating information overload. Despite remarkable …
基于深度学习的群组推荐方法研究综述
郑楠， 章颂， 刘玉桥， 王雨桐， 王飞跃 - 自动化学报, 2024 - aas.net.cn
群组推荐(Group recommendation) 在信息检索与数据挖掘领域近年来备受关注,
其旨在从海量候选集中挑选出一组用户可能感兴趣的项目. 随着深度学习技术的不断发展 …
其旨在从海量候选集中挑选出一组用户可能感兴趣的项目. 随着深度学习技术的不断发展 …
Performative Debias with Fair-exposure Optimization Driven by Strategic Agents in Recommender Systems
Data bias, eg, popularity impairs the dynamics of two-sided markets within recommender
systems. This overshadows the less visible but potentially intriguing long-tail items that could …
systems. This overshadows the less visible but potentially intriguing long-tail items that could …
A Knowledge-Centric Benchmarking Framework and Empirical Study for Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) enhances generative models by integrating
retrieval mechanisms, which allow these models to access and utilize external knowledge …
retrieval mechanisms, which allow these models to access and utilize external knowledge …
A Survey on Diffusion Models for Recommender Systems
While traditional recommendation techniques have made significant strides in the past
decades, they still suffer from limited generalization performance caused by factors like …
decades, they still suffer from limited generalization performance caused by factors like …
Pre-trained Language Model and Knowledge Distillation for Lightweight Sequential Recommendation
L Li, M Cheng, Z Liu, H Zhang, Q Liu… - arXiv preprint arXiv …, 2024 - arxiv.org
Sequential recommendation models user interests based on historical behaviors to provide
personalized recommendation. Previous sequential recommendation algorithms primarily …
personalized recommendation. Previous sequential recommendation algorithms primarily …