Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Review

A Vats, V Jain, R Raja, A Chadha - arXiv preprint arXiv:2402.18590, 2024 - arxiv.org
The paper underscores the significance of Large Language Models (LLMs) in reshaping
recommender systems, attributing their value to unique reasoning abilities absent in …

Prompting large language models for recommender systems: A comprehensive framework and empirical analysis

L Xu, J Zhang, B Li, J Wang, M Cai, WX Zhao… - arXiv preprint arXiv …, 2024 - arxiv.org
Recently, large language models such as ChatGPT have showcased remarkable abilities in
solving general tasks, demonstrating the potential for applications in recommender systems …

How can recommender systems benefit from large language models: A survey

J Lin, X Dai, Y Xi, W Liu, B Chen, H Zhang, Y Liu… - arXiv preprint arXiv …, 2023 - arxiv.org
With the rapid development of online services, recommender systems (RS) have become
increasingly indispensable for mitigating information overload. Despite remarkable …

Conditional denoising diffusion for sequential recommendation

Y Wang, Z Liu, L Yang, PS Yu - … on Knowledge Discovery and Data Mining, 2024 - Springer
Contemporary attention-based sequential recommendations often encounter the
oversmoothing problem, which generates indistinguishable representations. Although …

CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendation

J Wu, CC Chang, T Yu, Z He, J Wang, Y Hou… - Proceedings of the 30th …, 2024 - dl.acm.org
The long-tail recommendation is a challenging task for traditional recommender systems,
due to data sparsity and data imbalance issues. The recent development of large language …

A survey of generative search and recommendation in the era of large language models

Y Li, X Lin, W Wang, F Feng, L Pang, W Li, L Nie… - arXiv preprint arXiv …, 2024 - arxiv.org
With the information explosion on the Web, search and recommendation are foundational
infrastructures to satisfying users' information needs. As the two sides of the same coin, both …

When Search Engine Services meet Large Language Models: Visions and Challenges

H Xiong, J Bian, Y Li, X Li, M Du… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
Combining Large Language Models (LLMs) with search engine services marks a significant
shift in the field of services computing, opening up new possibilities to enhance how we …

AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning

J Zhang, T Lan, R Murthy, Z Liu, W Yao, J Tan… - arXiv preprint arXiv …, 2024 - arxiv.org
Autonomous agents powered by large language models (LLMs) have garnered significant
research attention. However, fully harnessing the potential of LLMs for agent-based tasks …

AgentLite: A Lightweight Library for Building and Advancing Task-Oriented LLM Agent System

Z Liu, W Yao, J Zhang, L Yang, Z Liu, J Tan… - arXiv preprint arXiv …, 2024 - arxiv.org
The booming success of LLMs initiates rapid development in LLM agents. Though the
foundation of an LLM agent is the generative model, it is critical to devise the optimal …

All roads lead to rome: Unveiling the trajectory of recommender systems across the llm era

B Chen, X Dai, H Guo, W Guo, W Liu, Y Liu… - arXiv preprint arXiv …, 2024 - arxiv.org
Recommender systems (RS) are vital for managing information overload and delivering
personalized content, responding to users' diverse information needs. The emergence of …