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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References

SHOWING 1-10 OF 34 REFERENCES

Deep Interest Network for Click-Through Rate Prediction

A novel model: Deep Interest Network (DIN) is proposed which tackles this challenge by designing a local activation unit to adaptively learn the representation of user interests from historical behaviors with respect to a certain ad.

KGAT: Knowledge Graph Attention Network for Recommendation

This work proposes a new method named Knowledge Graph Attention Network (KGAT), which explicitly models the high-order connectivities in KG in an end-to-end fashion and significantly outperforms state-of-the-art methods like Neural FM and RippleNet.

Explainable Reasoning over Knowledge Graphs for Recommendation

A new model named Knowledge-aware Path Recurrent Network (KPRN) is contributed to exploit knowledge graph for recommendation to allow effective reasoning on paths to infer the underlying rationale of a user-item interaction.

Deep Neural Networks for YouTube Recommendations

This paper details a deep candidate generation model and then describes a separate deep ranking model and provides practical lessons and insights derived from designing, iterating and maintaining a massive recommendation system with enormous user-facing impact.

Deep Session Interest Network for Click-Through Rate Prediction

A novel CTR model named Deep Session Interest Network (DSIN) is proposed that leverages users' multiple historical sessions in their behavior sequences and outperforms other state-of-the-art models on both datasets.

Knowledge Graph Convolutional Networks for Recommender Systems

This paper proposes Knowledge Graph Convolutional Networks (KGCN), an end-to-end framework that captures inter-item relatedness effectively by mining their associated attributes on the KG.

Deep Interest Evolution Network for Click-Through Rate Prediction

This paper proposes a novel model, named Deep Interest Evolution Network~(DIEN), for CTR prediction, which significantly outperforms the state-of-the-art solutions and design interest extractor layer to capture temporal interests from history behavior sequence.

Graph Attention Networks

We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior

Real-time context-aware social media recommendation

  • Xiangmin ZhouD. QinLei ChenYanchun Zhang
  • Computer Science
    The VLDB Journal
  • 2018
A novel approach based on the correlation between a feature and a group of other ones for selecting the optimal features used in recommendation, which fully removes the redundancy is proposed and a graph-based model called content–context interaction graph is proposed.

AliCoCo: Alibaba E-commerce Cognitive Concept Net

This paper proposes to construct a large-scale E-commerce Cognitive Concept Net named "AliCoCo", which is practiced in Alibaba, the largest Chinese e-commerce platform in the world, and presents details on how AliCoCo is constructed semi-automatically and its successful, ongoing and potential applications in e- commerce.