In recommender systems, knowledge graph (KG) can offer critical information
that is lacking in the original user-item interaction graph (IG). Recent
process has explored this direction and shows that contrastive learning is a
promising way to integrate both. However, we observe that existing KG-enhanced
recommenders struggle in balancing between the two contrastive views of IG and
KG, making them sometimes even less effective than simply applying contrastive
learning on IG without using KG. In this paper, we propose a new contrastive
learning framework for KG-enhanced recommendation. Specifically, to make full
use of the knowledge, we construct two separate contrastive views for KG and
IG, and maximize their mutual information; to ease the contrastive learning on
the two views, we further fuse KG information into IG in a one-direction
manner.Extensive experimental results on three real-world datasets demonstrate
the effectiveness and efficiency of our method, compared to the
state-of-the-art. Our code is available through the anonymous
link:this https URL

提出一种新的用于增强推荐系统的对比学习框架，通过构建两个独立的对比视图，最大化它们的相互信息，并以单向方式将知识图谱信息融入到用户 - 物品交互图中，实验证明该方法相比现有技术具有更好的效果和效率。

关于知识增强推荐的对比视角之甜蜜点

On the Sweet Spot of Contrastive Views for Knowledge-enhanced  Recommendation

Since Knowledge Graphs (KGs) contain rich semantic information, recently
there has been an influx of KG-enhanced recommendation methods. Most of
existing methods are entirely designed based on euclidean space without
considering curvature. However, recent studies have revealed that a tremendous
graph-structured data exhibits highly non-euclidean properties. Motivated by
these observations, in this work, we propose a knowledge-based multiple
adaptive spaces fusion method for recommendation, namely MCKG. Unlike existing
methods that solely adopt a specific manifold, we introduce the unified space
that is compatible with hyperbolic, euclidean and spherical spaces.
Furthermore, we fuse the multiple unified spaces in an attention manner to
obtain the high-quality embeddings for better knowledge propagation. In
addition, we propose a geometry-aware optimization strategy which enables the
pull and push processes benefited from both hyperbolic and spherical spaces.
Specifically, in hyperbolic space, we set smaller margins in the area near to
the origin, which is conducive to distinguishing between highly similar
positive items and negative ones. At the same time, we set larger margins in
the area far from the origin to ensure the model has sufficient error
tolerance. The similar manner also applies to spherical spaces. Extensive
experiments on three real-world datasets demonstrate that the MCKG has a
significant improvement over state-of-the-art recommendation methods. Further
ablation experiments verify the importance of multi-space fusion and
geometry-aware optimization strategy, justifying the rationality and
effectiveness of MCKG.

本研究提出了一种基于知识的多重自适应空间融合方法 (MCKG) 用于推荐，通过引入与双曲、欧氏和球形空间兼容的统一空间，并以注意机制融合多个统一空间，获得高质量嵌入以实现更好的知识传播。此外，提出了一种几何感知优化策略，使模型能够从双曲和球形空间中受益，通过在靠近原点的区域设置较小的边界值来区分高度相似的正向项目和负向项目，并在远离原点的区域设置较大的边界值以确保模型具有足够的误差容忍度。大量的实验结果表明，MCKG 在三个真实世界数据集上相较于最先进的推荐方法有了显著的改进。进一步的消融实验验证了多空间融合和几何感知优化策略的重要性，证明了 MCKG 的合理性和有效性。