mlsanityData to 14 Oct 2026

Impression-Informed Multi-Behavior Recommender System: A Hierarchical Graph Attention Approach

arXiv 2309.03169 ↗First posted 6 Sep 2023cs.IR cs.LGcited by 2

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Abstract (arXiv)

While recommender systems have significantly benefited from implicit feedback, they have often missed the nuances of multi-behavior interactions between users and items. Historically, these systems either amalgamated all behaviors, such as textitimpression (formerly textitview), textitadd-to-cart, and textitbuy, under a singular 'interaction' label, or prioritized only the target behavior, often the textitbuy action, discarding valuable auxiliary signals. Although recent advancements tried addressing this simplification, they primarily gravitated towards optimizing the target behavior alone, battling with data scarcity. Additionally, they tended to bypass the nuanced hierarchy intrinsic to behaviors. To bridge these gaps, we introduce the Hierarchical Multi-behavior Graph Attention Network (HMGN). This pioneering framework leverages attention mechanisms to discern information from both inter and intra-behaviors while employing a multi-task Hierarchical Bayesian Personalized Ranking (HBPR) for optimization. Recognizing the need for scalability, our approach integrates a specialized multi-behavior sub-graph sampling technique. Moreover, the adaptability of HMGN allows for the seamless inclusion of knowledge metadata and time-series data. Empirical results attest to our model's prowess, registering a notable performance boost of up to 64% in NDCG@100 metrics over conventional graph neural network methods.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 32 of its 51 references that are in the map. First posted on arXiv 6 Sep 2023.

Builds on

32 of 51 references.

References · 32

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20 more
  1. 2019Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networkscited by 177
  2. 2021Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendationcited by 173
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  8. 2021Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendationcited by 39
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  13. 2022Multi-Behavior Sequential Recommendation with Temporal Graph Transformercited by 20
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  15. 2022Less is More: Reweighting Important Spectral Graph Features for Recommendationcited by 10
  16. 2021On Estimating Recommendation Evaluation Metrics under Samplingcited by 9
  17. 2022Multi-Behavior Enhanced Recommendation with Cross-Interaction Collaborative Relation Modelingcited by 7
  18. 2022Towards Reliable Item Sampling for Recommendation Evaluationcited by 6
  19. 2021DMBGN: Deep Multi-Behavior Graph Networks for Voucher Redemption Rate Predictioncited by 3
  20. 2021Scalable Deep Graph Clustering with Random-walk based Self-supervised Learningcited by 2

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Cited by year

Citing papers per year: 2023: 2.

2 papers.

  1. 2023Revisiting Recommendation Loss Functions through Contrastive Learning (Technical Report)cited by 5
  2. 2023Group-Aware Interest Disentangled Dual-Training for Personalized Recommendationcited by 1