Recommender systems are the cornerstone of today's information dissemination,
yet a disconnect between offline metrics and online performance greatly hinders
their development. Addressing this challenge, we envision a recommendation
simulator, capitalizing on recent breakthroughs in human-level intelligence
exhibited by Large Language Models (LLMs). We propose Agent4Rec, a novel movie
recommendation simulator, leveraging LLM-empowered generative agents equipped
with user profile, memory, and actions modules specifically tailored for the
recommender system. In particular, these agents' profile modules are
initialized using the MovieLens dataset, capturing users' unique tastes and
social traits; memory modules log both factual and emotional memories and are
integrated with an emotion-driven reflection mechanism; action modules support
a wide variety of behaviors, spanning both taste-driven and emotion-driven
actions. Each agent interacts with personalized movie recommendations in a
page-by-page manner, relying on a pre-implemented collaborative filtering-based
recommendation algorithm. We delve into both the capabilities and limitations
of Agent4Rec, aiming to explore an essential research question: to what extent
can LLM-empowered generative agents faithfully simulate the behavior of real,
autonomous humans in recommender systems? Extensive and multi-faceted
evaluations of Agent4Rec highlight both the alignment and deviation between
agents and user-personalized preferences. Beyond mere performance comparison,
we explore insightful experiments, such as emulating the filter bubble effect
and discovering the underlying causal relationships in recommendation tasks.
Our codes are available at this https URL

基于大型语言模型（LLM）的 Agent4Rec 推荐系统模拟器能够模拟自主用户在电影推荐系统中的行为，并探索了推荐任务中的过滤泡沫效应和潜在因果关系的实验。