When large language models meet personalization: Perspectives of challenges and opportunities
The advent of large language models marks a revolutionary breakthrough in artificial
intelligence. With the unprecedented scale of training and model parameters, the capability …
intelligence. With the unprecedented scale of training and model parameters, the capability …
Recommender systems in the era of large language models (llms)
With the prosperity of e-commerce and web applications, Recommender Systems (RecSys)
have become an indispensable and important component, providing personalized …
have become an indispensable and important component, providing personalized …
A survey on large language models for recommendation
Abstract Large Language Models (LLMs) have emerged as powerful tools in the field of
Natural Language Processing (NLP) and have recently gained significant attention in the …
Natural Language Processing (NLP) and have recently gained significant attention in the …
Flashattention-3: Fast and accurate attention with asynchrony and low-precision
Attention, as a core layer of the ubiquitous Transformer architecture, is the bottleneck for
large language models and long-context applications. elaborated an approach to speed up …
large language models and long-context applications. elaborated an approach to speed up …
Recommendation as instruction following: A large language model empowered recommendation approach
In the past decades, recommender systems have attracted much attention in both research
and industry communities. Existing recommendation models mainly learn the underlying …
and industry communities. Existing recommendation models mainly learn the underlying …
Uncovering chatgpt's capabilities in recommender systems
The debut of ChatGPT has recently attracted significant attention from the natural language
processing (NLP) community and beyond. Existing studies have demonstrated that ChatGPT …
processing (NLP) community and beyond. Existing studies have demonstrated that ChatGPT …
Is chatgpt a good recommender? a preliminary study
Recommendation systems have witnessed significant advancements and have been widely
used over the past decades. However, most traditional recommendation methods are task …
used over the past decades. However, most traditional recommendation methods are task …
Text is all you need: Learning language representations for sequential recommendation
Sequential recommendation aims to model dynamic user behavior from historical
interactions. Existing methods rely on either explicit item IDs or general textual features for …
interactions. Existing methods rely on either explicit item IDs or general textual features for …
Large language models as zero-shot conversational recommenders
In this paper, we present empirical studies on conversational recommendation tasks using
representative large language models in a zero-shot setting with three primary …
representative large language models in a zero-shot setting with three primary …
Recommender systems with generative retrieval
Modern recommender systems perform large-scale retrieval by embedding queries and item
candidates in the same unified space, followed by approximate nearest neighbor search to …
candidates in the same unified space, followed by approximate nearest neighbor search to …