Recommender models excel at providing domain-specific item recommendations by
leveraging extensive user behavior data. Despite their ability to act as
lightweight domain experts, they struggle to perform versatile tasks such as
providing explanations and engaging in conversations. On the other hand, large
language models (LLMs) represent a significant step towards artificial general
intelligence, showcasing remarkable capabilities in instruction comprehension,
commonsense reasoning, and human interaction. However, LLMs lack the knowledge
of domain-specific item catalogs and behavioral patterns, particularly in areas
that diverge from general world knowledge, such as online e-commerce.
Finetuning LLMs for each domain is neither economic nor efficient.
In this paper, we bridge the gap between recommender models and LLMs,
combining their respective strengths to create a versatile and interactive
recommender system. We introduce an efficient framework called RecAgent, which
employs LLMs as the brain and recommender models as tools. We first outline a
minimal set of essential tools required to transform LLMs into RecAgent. We
then propose an efficient workflow within RecAgent for task execution,
incorporating key components such as a memory bus, dynamic
demonstration-augmented task planning, and reflection. RecAgent enables
traditional recommender systems, such as those ID-based matrix factorization
models, to become interactive systems with a natural language interface through
the integration of LLMs. Experimental results on several public datasets show
that RecAgent achieves satisfying performance as a conversational recommender
system, outperforming general-purpose LLMs.

通过将推荐模型和大型语言模型相结合，本文介绍了一个名为 RecAgent 的高效框架，旨在创建一个多功能和交互式的推荐系统，通过整合大型语言模型，将传统的推荐系统转化为具有自然语言界面的交互系统。实验结果表明，RecAgent 作为一个对话式推荐系统表现出令人满意的性能，优于通用的大型语言模型。

推荐 AI 代理：整合大型语言模型进行交互式推荐

Recommender AI Agent: Integrating Large Language Models for Interactive  Recommendations

Interactive recommender system (IRS) has drawn huge attention because of its
flexible recommendation strategy and the consideration of optimal long-term
user experiences. To deal with the dynamic user preference and optimize
accumulative utilities, researchers have introduced reinforcement learning (RL)
into IRS. However, RL methods share a common issue of sample efficiency, i.e.,
huge amount of interaction data is required to train an effective
recommendation policy, which is caused by the sparse user responses and the
large action space consisting of a large number of candidate items. Moreover,
it is infeasible to collect much data with explorative policies in online
environments, which will probably harm user experience. In this work, we
investigate the potential of leveraging knowledge graph (KG) in dealing with
these issues of RL methods for IRS, which provides rich side information for
recommendation decision making. Instead of learning RL policies from scratch,
we make use of the prior knowledge of the item correlation learned from KG to
(i) guide the candidate selection for better candidate item retrieval, (ii)
enrich the representation of items and user states, and (iii) propagate user
preferences among the correlated items over KG to deal with the sparsity of
user feedback. Comprehensive experiments have been conducted on two real-world
datasets, which demonstrate the superiority of our approach with significant
improvements against state-of-the-arts.

本研究探讨了在交互式推荐系统中，如何使用知识图谱来解决强化学习中的样本效率问题以及用户反馈稀疏问题，具体包括引导候选项选择，增强项和用户状态的表示和传递用户偏好。实验结果表明，该方法具有显著的改进优势。