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 important component of our daily life, providing personalized suggestions …
have become an important component of our daily life, providing personalized suggestions …
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 in our daily lives, providing …
have become an indispensable and important component in our daily lives, providing …
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 …
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 …
Time-llm: Time series forecasting by reprogramming large language models
Time series forecasting holds significant importance in many real-world dynamic systems
and has been extensively studied. Unlike natural language process (NLP) and computer …
and has been extensively studied. Unlike natural language process (NLP) and computer …
Agentcf: Collaborative learning with autonomous language agents for recommender systems
Recently, there has been an emergence of employing LLM-powered agents as believable
human proxies, based on their remarkable decision-making capability. However, existing …
human proxies, based on their remarkable decision-making capability. However, existing …
Prompt-augmented temporal point process for streaming event sequence
Abstract Neural Temporal Point Processes (TPPs) are the prevalent paradigm for modeling
continuous-time event sequences, such as user activities on the web and financial …
continuous-time event sequences, such as user activities on the web and financial …
A causal explainable guardrails for large language models
Large Language Models (LLMs) have shown impressive performance in natural language
tasks, but their outputs can exhibit undesirable attributes or biases. Existing methods for …
tasks, but their outputs can exhibit undesirable attributes or biases. Existing methods for …
Prompting large language models for recommender systems: A comprehensive framework and empirical analysis
Recently, large language models such as ChatGPT have showcased remarkable abilities in
solving general tasks, demonstrating the potential for applications in recommender systems …
solving general tasks, demonstrating the potential for applications in recommender systems …
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 …