Recommender systems have seen significant advancements with the influence of
deep learning and graph neural networks, particularly in capturing complex
user-item relationships. However, these graph-based recommenders heavily depend
on ID-based data, potentially disregarding valuable textual information
associated with users and items, resulting in less informative learned
representations. Moreover, the utilization of implicit feedback data introduces
potential noise and bias, posing challenges for the effectiveness of user
preference learning. While the integration of large language models (LLMs) into
traditional ID-based recommenders has gained attention, challenges such as
scalability issues, limitations in text-only reliance, and prompt input
constraints need to be addressed for effective implementation in practical
recommender systems. To address these challenges, we propose a model-agnostic
framework RLMRec that aims to enhance existing recommenders with LLM-empowered
representation learning. It proposes a recommendation paradigm that integrates
representation learning with LLMs to capture intricate semantic aspects of user
behaviors and preferences. RLMRec incorporates auxiliary textual signals,
develops a user/item profiling paradigm empowered by LLMs, and aligns the
semantic space of LLMs with the representation space of collaborative
relational signals through a cross-view alignment framework. This work further
establish a theoretical foundation demonstrating that incorporating textual
signals through mutual information maximization enhances the quality of
representations. In our evaluation, we integrate RLMRec with state-of-the-art
recommender models, while also analyzing its efficiency and robustness to noise
data. Our implementation codes are available at
this https URL

通过整合大型语言模型与推荐系统的表示学习，我们提出了一个模型不可知的框架 RLMRec，旨在增强现有的推荐器，并提高其对于用户行为和偏好的语义理解，同时通过交叉视角对齐框架，将 LLM 的语义空间与协同关系信号的表示空间相对应。

利用大型语言模型进行推荐的表示学习

Representation Learning with Large Language Models for Recommendation

Recent research on discourse relations has found that they are cued not only
by discourse markers (DMs) but also by other textual signals and that signaling
information is indicative of genres. While several corpora exist with discourse
relation signaling information such as the Penn Discourse Treebank (PDTB,
Prasad et al. 2008) and the Rhetorical Structure Theory Signalling Corpus
(RST-SC, Das and Taboada 2018), they both annotate the Wall Street Journal
(WSJ) section of the Penn Treebank (PTB, Marcus et al. 1993), which is limited
to the news domain. Thus, this paper adapts the signal identification and
anchoring scheme (Liu and Zeldes, 2019) to three more genres, examines the
distribution of signaling devices across relations and genres, and provides a
taxonomy of indicative signals found in this dataset.

本研究通过将 Liu 和 Zeldes（2019）的信号识别和定位方案适用于三种不同语体，检查了信号装置在关系和语体中的分布，并提供了在此数据集中发现的指示性信号的分类。