Masked Graph Transformer for Large-Scale Recommendation
arxiv(2024)
摘要
Graph Transformers have garnered significant attention for learning
graph-structured data, thanks to their superb ability to capture long-range
dependencies among nodes. However, the quadratic space and time complexity
hinders the scalability of Graph Transformers, particularly for large-scale
recommendation. Here we propose an efficient Masked Graph Transformer, named
MGFormer, capable of capturing all-pair interactions among nodes with a linear
complexity. To achieve this, we treat all user/item nodes as independent
tokens, enhance them with positional embeddings, and feed them into a
kernelized attention module. Additionally, we incorporate learnable relative
degree information to appropriately reweigh the attentions. Experimental
results show the superior performance of our MGFormer, even with a single
attention layer.
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