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。

关于推荐中的生成代理

On Generative Agents in Recommendation

In this work, we establish a baseline potential for how modern
model-generated text explanations of movie recommendations may help users, and
explore what different components of these text explanations that users like or
dislike, especially in contrast to existing human movie reviews. We found that
participants gave no significantly different rankings between movies, nor did
they give significantly different individual quality scores to reviews of
movies that they had never seen before. However, participants did mark reviews
as significantly better when they were movies they had seen before. We also
explore specific aspects of movie review texts that participants marked as
important for each quality. Overall, we establish that modern LLMs are a
promising source of recommendation explanations, and we intend on further
exploring personalizable text explanations in the future.

我们研究了现代模型生成的电影推荐的文本解释如何帮助用户，以及探讨用户对这些文本解释的不同组成部分的喜好和厌恶，特别是与现有的人类电影评论相比。我们发现，参与者对电影之间的排名以及对从未看过的电影评论的个人质量评分没有显著差异。然而，当评论是关于他们之前看过的电影时，参与者标记为更好的评论。此外，我们还探讨了参与者认为每个质量重要的电影评论文本的具体方面。总的来说，我们证明了现代 LLMs 是有希望作为推荐解释的来源，我们打算在未来进一步探索可个性化的文本解释。

以 GPT 为基准的推荐解释文本

GPT as a Baseline for Recommendation Explanation Texts

Large language models (LLMs) have achieved impressive zero-shot performance
in various natural language processing (NLP) tasks, demonstrating their
capabilities for inference without training examples. Despite their success, no
research has yet explored the potential of LLMs to perform next-item
recommendations in the zero-shot setting. We have identified two major
challenges that must be addressed to enable LLMs to act effectively as
recommenders. First, the recommendation space can be extremely large for LLMs,
and LLMs do not know about the target user's past interacted items and
preferences. To address this gap, we propose a prompting strategy called
Zero-Shot Next-Item Recommendation (NIR) prompting that directs LLMs to make
next-item recommendations. Specifically, the NIR-based strategy involves using
an external module to generate candidate items based on user-filtering or
item-filtering. Our strategy incorporates a 3-step prompting that guides GPT-3
to carry subtasks that capture the user's preferences, select representative
previously watched movies, and recommend a ranked list of 10 movies. We
evaluate the proposed approach using GPT-3 on MovieLens 100K dataset and show
that it achieves strong zero-shot performance, even outperforming some strong
sequential recommendation models trained on the entire training dataset. These
promising results highlight the ample research opportunities to use LLMs as
recommenders. The code can be found at
this https URL

研究发现，使用 Zero-Shot Next-Item Recommendation (NIR) 方法可以使 GPT-3 模型在没有用户历史交互记录信息的情况下，成功地推荐电影，且在电影推荐方面，它甚至可以胜过一些训练有素的顺序推荐模型。

使用预训练的大型语言模型进行零样本下一项推荐

Zero-Shot Next-Item Recommendation using Large Pretrained Language  Models

Recommender systems have become ubiquitous in the past years. They solve the
tyranny of choice problem faced by many users, and are employed by many online
businesses to drive engagement and sales. Besides other criticisms, like
creating filter bubbles within social networks, recommender systems are often
reproved for collecting considerable amounts of personal data. However, to
personalize recommendations, personal information is fundamentally required. A
recent distributed learning scheme called federated learning has made it
possible to learn from personal user data without its central collection.
Accordingly, we present a complete recommender system for movie
recommendations, which provides privacy and thus trustworthiness on two levels:
First, it is trained using federated learning and thus is, by its very nature,
privacy-preserving, while still enabling individual users to benefit from
global insights. And second, a novel federated learning scheme, FedQ, is
employed, which not only addresses the problem of non-i.i.d. and small local
datasets, but also prevents input data reconstruction attacks by aggregating
client models early. To reduce the communication overhead, compression is
applied, which significantly reduces the exchanged neural network updates to a
fraction of their original data. We conjecture that it may also improve data
privacy through its lossy quantization stage.

本研究提出了一种基于联邦学习的电影推荐系统，采用新颖的联邦学习方案 FedQ, 通过量化和压缩阶段来保护隐私和减少通信开销，从而创建出同时保护隐私和提供个性化推荐的学习模型。