Graph neural networks (GNNs) are currently one of the most performant collaborative filtering methods. Meanwhile, owing to the use of an embedding table to represent each user/item as a distinct vector, GNN-based recommenders have inherited the long-standing defect of parameter inefficiency. As a common practice for scalable embeddings, parameter sharing enables the use of fewer embedding vectors (i.e., meta-embeddings). When assigning meta-embeddings, most existing methods are a heuristically designed, predefined mapping from each user's/item's ID to the corresponding meta-embedding indexes, thus simplifying the optimization problem into learning only the meta-embeddings. However, in the context of GNN-based collaborative filtering, such a fixed mapping omits the semantic correlations between entities that are evident in the user-item interaction graph, leading to suboptimal recommendation performance. To this end, we propose Lightweight Embeddings for Graph Collaborative Filtering (LEGCF), a parameter-efficient embedding framework dedicated to GNN-based recommenders. LEGCF innovatively introduces an assignment matrix as an extra learnable component on top of meta-embeddings. To jointly optimize these two heavily entangled components, aside from learning the meta-embeddings by minimizing the recommendation loss, LEGCF further performs efficient assignment update by enforcing a novel semantic similarity constraint and finding its closed-form solution based on matrix pseudo-inverse. The meta-embeddings and assignment matrix are alternately updated, where the latter is sparsified on the fly to ensure negligible storage overhead. Extensive experiments on three benchmark datasets have verified LEGCF's smallest trade-off between size and performance, with consistent accuracy gain over state-of-the-art baselines. The codebase of LEGCF is available in https://github.com/xurong-liang/LEGCF.

中文翻译：

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用于图协同过滤的轻量级嵌入

图神经网络（GNN）是目前性能最好的协同过滤方法之一。同时，由于使用嵌入表将每个用户/项目表示为不同的向量，基于 GNN 的推荐器继承了长期存在的参数效率低下的缺陷。作为可扩展嵌入的常见做法，参数共享允许使用更少的嵌入向量（即元嵌入）。在分配元嵌入时，大多数现有方法都是启发式设计的、从每个用户/项目的 ID 到相应元嵌入索引的预定义映射，从而将优化问题简化为仅学习元嵌入。然而，在基于 GNN 的协同过滤的背景下，这种固定映射忽略了用户-项目交互图中明显的实体之间的语义相关性，导致推荐性能不佳。为此，我们提出了图协同过滤的轻量级嵌入（LEGCF），这是一种专用于基于 GNN 的推荐器的参数高效嵌入框架。 LEGCF 创新性地引入了分配矩阵作为元嵌入之上的额外可学习组件。为了联合优化这两个严重纠缠的组件，除了通过最小化推荐损失来学习元嵌入之外，LEGCF 还通过强制执行新颖的语义相似性约束并基于矩阵伪逆找到其封闭形式解决方案来进一步执行有效的分配更新。元嵌入和分配矩阵交替更新，其中后者是动态稀疏的，以确保存储开销可以忽略不计。对三个基准数据集的大量实验验证了 LEGCF 在大小和性能之间的最小权衡，并且与最先进的基线相比具有一致的精度增益。 LEGCF 的代码库可在 https://github.com/xurong-liang/LEGCF 中找到。