Sequential recommendation systems (SRS) serve the purpose of predicting
users' subsequent preferences based on their past interactions and have been
applied across various domains such as e-commerce and social networking
platforms. However, practical SRS encounters challenges due to the fact that
most users engage with only a limited number of items, while the majority of
items are seldom consumed. These challenges, termed as the long-tail user and
long-tail item dilemmas, often create obstacles for traditional SRS methods.
Mitigating these challenges is crucial as they can significantly impact user
satisfaction and business profitability. While some research endeavors have
alleviated these issues, they still grapple with issues such as seesaw or noise
stemming from the scarcity of interactions. The emergence of large language
models (LLMs) presents a promising avenue to address these challenges from a
semantic standpoint. In this study, we introduce the Large Language Models
Enhancement framework for Sequential Recommendation (LLM-ESR), which leverages
semantic embeddings from LLMs to enhance SRS performance without increasing
computational overhead. To combat the long-tail item challenge, we propose a
dual-view modeling approach that fuses semantic information from LLMs with
collaborative signals from traditional SRS. To address the long-tail user
challenge, we introduce a retrieval augmented self-distillation technique to
refine user preference representations by incorporating richer interaction data
from similar users. Through comprehensive experiments conducted on three
authentic datasets using three widely used SRS models, our proposed enhancement
framework demonstrates superior performance compared to existing methodologies.

研究通过引入大型语言模型增强顺序推荐系统的性能，以应对长尾用户和长尾商品的挑战，并提出了融合语义信息和协同信号的双视图建模方法来解决这些问题。同时，通过使用检索增强的自蒸馏技术，对用户偏好表示进行改进。实验证明，所提出的增强框架相比现有方法表现更优。

大型语言模型增强的长尾用户和物品的顺序推荐

Large Language Models Enhanced Sequential Recommendation for Long-tail  User and Item

Physics-informed Graph Neural Networks have achieved remarkable performance
in learning through graph-structured data by mitigating common GNN challenges
such as over-smoothing, over-squashing, and heterophily adaption. Despite these
advancements, the development of a simple yet effective paradigm that
appropriately integrates previous methods for handling all these challenges is
still underway. In this paper, we draw an analogy between the propagation of
GNNs and particle systems in physics, proposing a model-agnostic enhancement
framework. This framework enriches the graph structure by introducing
additional nodes and rewiring connections with both positive and negative
weights, guided by node labeling information. We theoretically verify that GNNs
enhanced through our approach can effectively circumvent the over-smoothing
issue and exhibit robustness against over-squashing. Moreover, we conduct a
spectral analysis on the rewired graph to demonstrate that the corresponding
GNNs can fit both homophilic and heterophilic graphs. Empirical validations on
benchmarks for homophilic, heterophilic graphs, and long-term graph datasets
show that GNNs enhanced by our method significantly outperform their original
counterparts.

通过在图结构数据中学习，物理启发图神经网络在解决常见图神经网络挑战（如平滑过度、压缩过度和异质适应）方面取得了显著的性能。本文通过在传播过程中引入额外的节点和重新连接具有正负权重的连接来丰富图结构，在理论上验证了通过我们的方法增强的图神经网络可以有效地解决平滑过度问题，并对压缩过度具有鲁棒性。此外，我们对重连后的图进行谱分析，证明相应的图神经网络可以适应同质化和异质化的图。在同质化、异质化图以及长期图数据集的基准验证中，我们通过我们的方法增强的图神经网络显著优于原始版本。

设计您自己的宇宙：一种增强图神经网络的物理知识无偏方法

Design Your Own Universe: A Physics-Informed Agnostic Method for  Enhancing Graph Neural Networks

Noise is a pervasive element within real-world measurement data,
significantly undermining the performance of network inference models. However,
the quest for a comprehensive enhancement framework capable of bolstering noise
resistance across a diverse array of network inference models has remained
elusive. Here, we present an elegant and efficient framework tailored to
amplify the capabilities of network inference models in the presence of noise.
Leveraging curriculum learning, we mitigate the deleterious impact of noisy
samples on network inference models. Our proposed framework is model-agnostic,
seamlessly integrable into a plethora of model-based and model-free network
inference methods. Notably, we utilize one model-based and three model-free
network inference methods as the foundation. Extensive experimentation across
various synthetic and real-world networks, encapsulating diverse nonlinear
dynamic processes, showcases substantial performance augmentation under varied
noise types, particularly thriving in scenarios enriched with clean samples.
This framework's adeptness in fortifying both model-free and model-based
network inference methodologies paves the avenue towards a comprehensive and
unified enhancement framework, encompassing the entire spectrum of network
inference models. Available Code: this https URL

我们提出了一个优雅而高效的框架，通过结合课程学习来增强噪声存在下网络推断模型的性能，该框架能够适用于多种基于模型和不基于模型的网络推断方法，并在合成和真实网络中进行了广泛的实验验证。