Lightweight Embeddings for Graph Collaborative Filtering
arxiv(2024)
摘要
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.
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