Retrieval-augmented generation for large language models: A survey
Y Gao, Y Xiong, X Gao, K Jia, J Pan, Y Bi, Y Dai… - arXiv preprint arXiv …, 2023 - arxiv.org
Large language models (LLMs) demonstrate powerful capabilities, but they still face
challenges in practical applications, such as hallucinations, slow knowledge updates, and …
challenges in practical applications, such as hallucinations, slow knowledge updates, and …
From matching to generation: A survey on generative information retrieval
Information Retrieval (IR) systems are crucial tools for users to access information, widely
applied in scenarios like search engines, question answering, and recommendation …
applied in scenarios like search engines, question answering, and recommendation …
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 …
EAGER: Two-Stream Generative Recommender with Behavior-Semantic Collaboration
Generative retrieval has recently emerged as a promising approach to sequential
recommendation, framing candidate item retrieval as an autoregressive sequence …
recommendation, framing candidate item retrieval as an autoregressive sequence …
Planning ahead in generative retrieval: Guiding autoregressive generation through simultaneous decoding
This paper introduces PAG-a novel optimization and decoding approach that guides
autoregressive generation of document identifiers in generative retrieval models through …
autoregressive generation of document identifiers in generative retrieval models through …
All Roads Lead to Rome: Unveiling the Trajectory of Recommender Systems Across the LLM Era
Recommender systems (RS) are vital for managing information overload and delivering
personalized content, responding to users' diverse information needs. The emergence of …
personalized content, responding to users' diverse information needs. The emergence of …
Vector Quantization for Recommender Systems: A Review and Outlook
Vector quantization, renowned for its unparalleled feature compression capabilities, has
been a prominent topic in signal processing and machine learning research for several …
been a prominent topic in signal processing and machine learning research for several …
Multi-Behavior Generative Recommendation
Multi-behavior sequential recommendation (MBSR) aims to incorporate behavior types of
interactions for better recommendations. Existing approaches focus on the next-item …
interactions for better recommendations. Existing approaches focus on the next-item …
Bootstrapped Pre-training with Dynamic Identifier Prediction for Generative Retrieval
Generative retrieval uses differentiable search indexes to directly generate relevant
document identifiers in response to a query. Recent studies have highlighted the potential of …
document identifiers in response to a query. Recent studies have highlighted the potential of …
STORE: Streamlining Semantic Tokenization and Generative Recommendation with A Single LLM
Q Liu, J Zhu, L Fan, Z Zhao, XM Wu - arXiv preprint arXiv:2409.07276, 2024 - arxiv.org
Traditional recommendation models often rely on unique item identifiers (IDs) to distinguish
between items, which can hinder their ability to effectively leverage item content information …
between items, which can hinder their ability to effectively leverage item content information …