Graph Collaborative Filtering (GCF), one of the most widely adopted
recommendation system methods, effectively captures intricate relationships
between user and item interactions. Graph Contrastive Learning (GCL) based GCF
has gained significant attention as it leverages self-supervised techniques to
extract valuable signals from real-world scenarios. However, many methods
usually learn the instances of discrimination tasks that involve the
construction of contrastive pairs through random sampling. GCL approaches
suffer from sampling bias issues, where the negatives might have a semantic
structure similar to that of the positives, thus leading to a loss of effective
feature representation. To address these problems, we present the
\underline{Proto}typical contrastive learning through \underline{A}lignment and
\underline{U}niformity for recommendation, which is called \textbf{ProtoAU}.
Specifically, we first propose prototypes (cluster centroids) as a latent space
to ensure consistency across different augmentations from the origin graph,
aiming to eliminate the need for random sampling of contrastive pairs.
Furthermore, the absence of explicit negatives means that directly optimizing
the consistency loss between instance and prototype could easily result in
dimensional collapse issues. Therefore, we propose aligning and maintaining
uniformity in the prototypes of users and items as optimization objectives to
prevent falling into trivial solutions. Finally, we conduct extensive
experiments on four datasets and evaluate their performance on the task of link
prediction. Experimental results demonstrate that the proposed ProtoAU
outperforms other representative methods. The source codes of our proposed
ProtoAU are available at https://github.com/oceanlvr/ProtoAU.

通过在潜在空间中提出原型 (簇中心) 作为多样性图形的不同扩充的一致性基准，我们介绍了一种名为 ProtoAU 的推荐方法，它通过排序和保持用户和项目的原型的一致性以及统一性来解决了图对比学习中的采样偏差问题。

推荐系统中的原型对比学习：通过对齐和一致性实现

Prototypical Contrastive Learning through Alignment and Uniformity for  Recommendation

Graph collaborative filtering, which learns user and item representations
through message propagation over the user-item interaction graph, has been
shown to effectively enhance recommendation performance. However, most current
graph collaborative filtering models mainly construct the interaction graph on
a single behavior domain (e.g. click), even though users exhibit various types
of behaviors on real-world platforms, including actions like click, cart, and
purchase. Furthermore, due to variations in user engagement, there exists an
imbalance in the scale of different types of behaviors. For instance, users may
click and view multiple items but only make selective purchases from a small
subset of them. How to alleviate the behavior imbalance problem and utilize
information from the multiple behavior graphs concurrently to improve the
target behavior conversion (e.g. purchase) remains underexplored. To this end,
we propose IMGCF, a simple but effective model to alleviate behavior data
imbalance for multi-behavior graph collaborative filtering. Specifically, IMGCF
utilizes a multi-task learning framework for collaborative filtering on
multi-behavior graphs. Then, to mitigate the data imbalance issue, IMGCF
improves representation learning on the sparse behavior by leveraging
representations learned from the behavior domain with abundant data volumes.
Experiments on two widely-used multi-behavior datasets demonstrate the
effectiveness of IMGCF.

通过消息传递在用户 - 物品交互图上学习用户和物品表示的图协同过滤已被证明有效地增强推荐性能。然而，大多数当前的图协同过滤模型主要在单一行为域（例如点击）上构建交互图，尽管用户在现实世界的平台上展示了各种类型的行为，包括点击、购物车和购买等操作。此外，由于用户参与度的变化，不同类型行为之间存在不平衡。为了解决行为不平衡问题并同时利用来自多个行为图的信息以提高目标行为转化（例如购买），本文提出了一个简单而有效的模型 IMGCF，用于减轻多行为图协同过滤的行为数据不平衡问题。具体而言，IMGCF 利用多任务学习框架在多行为图上进行协同过滤。然后，为了解决数据不平衡问题，IMGCF 通过利用从具有大量数据量的行为域中学到的表示来改进稀疏行为的表示学习。对两个广泛使用的多行为数据集的实验证明了 IMGCF 的有效性。

缓解多行为图协同过滤中的行为数据不平衡

Alleviating Behavior Data Imbalance for Multi-Behavior Graph  Collaborative Filtering

Recently, graph collaborative filtering methods have been proposed as an
effective recommendation approach, which can capture users' preference over
items by modeling the user-item interaction graphs. In order to reduce the
influence of data sparsity, contrastive learning is adopted in graph
collaborative filtering for enhancing the performance. However, these methods
typically construct the contrastive pairs by random sampling, which neglect the
neighboring relations among users (or items) and fail to fully exploit the
potential of contrastive learning for recommendation. To tackle the above
issue, we propose a novel contrastive learning approach, named
Neighborhood-enriched Contrastive Learning, named NCL, which explicitly
incorporates the potential neighbors into contrastive pairs. Specifically, we
introduce the neighbors of a user (or an item) from graph structure and
semantic space respectively. For the structural neighbors on the interaction
graph, we develop a novel structure-contrastive objective that regards users
(or items) and their structural neighbors as positive contrastive pairs. In
implementation, the representations of users (or items) and neighbors
correspond to the outputs of different GNN layers. Furthermore, to excavate the
potential neighbor relation in semantic space, we assume that users with
similar representations are within the semantic neighborhood, and incorporate
these semantic neighbors into the prototype-contrastive objective. The proposed
NCL can be optimized with EM algorithm and generalized to apply to graph
collaborative filtering methods. Extensive experiments on five public datasets
demonstrate the effectiveness of the proposed NCL, notably with 26% and 17%
performance gain over a competitive graph collaborative filtering base model on
the Yelp and Amazon-book datasets respectively. Our code is available at:
this https URL.

本文提出一种名为邻域增强对比学习（Neighborhood-enriched Contrastive Learning，简称 NCL）的方法，将邻居嵌入到对比对中，以实现在图协同过滤方法中减少数据稀疏性的效果，并在五个公共数据集上进行广泛的实验证明了该方法的有效性，尤其在 Yelp 和 Amazon-book 数据集上分别获得了 26％和 17％的性能提升。