Multi-Behavior Collaborative Filtering with Partial Order Graph Convolutional Networks
CoRR(2024)
摘要
Representing the information of multiple behaviors in the single graph
collaborative filtering (CF) vector has been a long-standing challenge. This is
because different behaviors naturally form separate behavior graphs and learn
separate CF embeddings. Existing models merge the separate embeddings by
appointing the CF embeddings for some behaviors as the primary embedding and
utilizing other auxiliaries to enhance the primary embedding. However, this
approach often results in the joint embedding performing well on the main tasks
but poorly on the auxiliary ones. To address the problem arising from the
separate behavior graphs, we propose the concept of Partial Order Graphs (POG).
POG defines the partial order relation of multiple behaviors and models
behavior combinations as weighted edges to merge separate behavior graphs into
a joint POG. Theoretical proof verifies that POG can be generalized to any
given set of multiple behaviors. Based on POG, we propose the tailored Partial
Order Graph Convolutional Networks (POGCN) that convolute neighbors'
information while considering the behavior relations between users and items.
POGCN also introduces a partial-order BPR sampling strategy for efficient and
effective multiple-behavior CF training. POGCN has been successfully deployed
on the homepage of Alibaba for two months, providing recommendation services
for over one billion users. Extensive offline experiments conducted on three
public benchmark datasets demonstrate that POGCN outperforms state-of-the-art
multi-behavior baselines across all types of behaviors. Furthermore, online A/B
tests confirm the superiority of POGCN in billion-scale recommender systems.
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