The long-tail recommendation is a challenging task for traditional
recommender systems, due to data sparsity and data imbalance issues. The recent
development of large language models (LLMs) has shown their abilities in
complex reasoning, which can help to deduce users' preferences based on very
few previous interactions. However, since most LLM-based systems rely on items'
semantic meaning as the sole evidence for reasoning, the collaborative
information of user-item interactions is neglected, which can cause the LLM's
reasoning to be misaligned with task-specific collaborative information of the
dataset. To further align LLMs' reasoning to task-specific user-item
interaction knowledge, we introduce collaborative retrieval-augmented LLMs,
CoRAL, which directly incorporate collaborative evidence into the prompts.
Based on the retrieved user-item interactions, the LLM can analyze shared and
distinct preferences among users, and summarize the patterns indicating which
types of users would be attracted by certain items. The retrieved collaborative
evidence prompts the LLM to align its reasoning with the user-item interaction
patterns in the dataset. However, since the capacity of the input prompt is
limited, finding the minimally-sufficient collaborative information for
recommendation tasks can be challenging. We propose to find the optimal
interaction set through a sequential decision-making process and develop a
retrieval policy learned through a reinforcement learning (RL) framework,
CoRAL. Our experimental results show that CoRAL can significantly improve LLMs'
reasoning abilities on specific recommendation tasks. Our analysis also reveals
that CoRAL can more efficiently explore collaborative information through
reinforcement learning.

通过协同检索增强的大型语言模型，将合作证据直接融入提问中以使其对数据集中的用户 - 项目交互模式进行联合推理，从而在特定的推荐任务上显著提高了大型语言模型的推理能力，并且可以通过强化学习更有效地探索协作信息。

CoRAL：协作检索增强大型语言模型改进长尾推荐

CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve  Long-tail Recommendation

Session-based recommendation focuses on the prediction of user actions based
on anonymous sessions and is a necessary method in the lack of user historical
data. However, none of the existing session-based recommendation methods
explicitly takes the long-tail recommendation into consideration, which plays
an important role in improving the diversity of recommendation and producing
the serendipity. As the distribution of items with long-tail is prevalent in
session-based recommendation scenarios (e.g., e-commerce, music, and TV program
recommendations), more attention should be put on the long-tail session-based
recommendation. In this paper, we propose a novel network architecture, namely
TailNet, to improve long-tail recommendation performance, while maintaining
competitive accuracy performance compared with other methods. We start by
classifying items into short-head (popular) and long-tail (niche) items based
on click frequency. Then a novel is proposed and applied in TailNet to
determine user preference between two types of items, so as to softly adjust
and personalize recommendations. Extensive experiments on two real-world
datasets verify the superiority of our method compared with state-of-the-art
works.

本文提出了一种新的网络架构 TailNet，将项目分类为短头和长尾项目并根据点击频率确定用户偏好，以软调整和个性化推荐，以提高长尾推荐的性能，同时保持与其他方法的竞争精度性能相当，通过两个真实世界数据集的广泛实验验证了我们方法的优越性。