ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation

@article{Feng2020ATBRGAT,
  title={ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation},
  author={Yufei Feng and Binbin Hu and Fuyu Lv and Qingwen Liu and Zhiqiang Zhang and Wenwu Ou},
  journal={Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval},
  year={2020}
}
  • Yufei FengBinbin Hu Wenwu Ou
  • Published 25 May 2020
  • Computer Science
  • Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval
Recommender system (RS) devotes to predicting user preference to a given item and has been widely deployed in most web-scale applications. Recently, knowledge graph (KG) attracts much attention in RS due to its abundant connective information. Existing methods either explore independent meta-paths for user-item pairs over KG, or employ graph neural network (GNN) on whole KG to produce representations for users and items separately. Despite effectiveness, the former type of methods fails to… 
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16 Citations
Highly Influential Citations
2
Background Citations
8
Methods Citations
4

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16 Citations

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DSKReG: Differentiable Sampling on Knowledge Graph for Recommendation with Relational GNN

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From Knowledge Graph Development to Serving Industrial Knowledge Automation: A Review

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TKGAT: Graph attention network for knowledge-enhanced tag-aware recommendation system

Dynamic Hypergraph Convolutional Network

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Biomedical Relation Extraction With Knowledge Graph-Based Recommendations

Adding recommendations from KG-based recommendation improves the system’s ability to identify true relations that the baseline deep RE model could not extract from the text, and develops a new RE system, named K-BiOnt.

PICASSO: Unleashing the Potential of GPU-centric Training for Wide-and-deep Recommender Systems

A novel framework named PICASSO is proposed to accelerate the training of recommendation models on commodity hardware and leverage the model structure and data distribution to unleash the potential of hardware through the authors' packing, interleaving, and caching optimization.

Triangle Graph Interest Network for Click-through Rate Prediction

This paper points out two special properties of triangles in the item-item graphs for recommendation systems: Intra-triangle homophily and Inter-triangles heterophiy and proposes a novel and effective framework named Triangle Graph Interest Network (TGIN).

Is the suggested food your desired?: Multi-modal recipe recommendation with demand-based knowledge graph

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