Lightweight Embeddings for Graph Collaborative Filtering

Graph neural networks (GNNs) are currently one of the most performant and versatile collaborative filtering methods. Meanwhile, like in traditional collaborative filtering, owing to the use of an embedding table to represent each user/item entity as a distinct vector, GNN-based recommenders have inherited its long-standing defect of parameter inefficiency. As a common practice for scalable embeddings, parameter sharing enables the use of fewer embedding vectors (which we term meta-embeddings), where each entity is represented by a unique combination of meta-embeddings instead. When assigning meta-embeddings, most existing methods are a heuristically designed, predefined mapping from each user/item entity's ID to the corresponding meta-embedding indexes (e.g., double hashing), 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 additional 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.