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    <abstract>News recommendation (NR) is essential for online news services. Existing NR methods typically adopt a news-user representation learning framework, facing two potential limitations. First, in news encoder, single candidate news encoding suffers from an insufficient semantic information problem. Second, existing graph-based NR methods are promising but lack effective news-user feature interaction, rendering the graph-based recommendation suboptimal. To overcome these limitations, we propose dual-interactive graph attention networks (DIGAT) consisting of news- and user-graph channels. In the news-graph channel, we enrich the semantics of single candidate news by incorporating the semantically relevant news information with a semantic-augmented graph (SAG). In the user-graph channel, multi-level user interests are represented with a news-topic graph. Most notably, we design a dual-graph interaction process to perform effective feature interaction between the news and user graphs, which facilitates accurate news-user representation matching. Experiment results on the benchmark dataset MIND show that DIGAT outperforms existing news recommendation methods. Further ablation studies and analyses validate the effectiveness of (1) semantic-augmented news graph modeling and (2) dual-graph interaction.</abstract>
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%0 Conference Proceedings
%T DIGAT: Modeling News Recommendation with Dual-Graph Interaction
%A Mao, Zhiming
%A Li, Jian
%A Wang, Hongru
%A Zeng, Xingshan
%A Wong, Kam-Fai
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F mao-etal-2022-digat
%X News recommendation (NR) is essential for online news services. Existing NR methods typically adopt a news-user representation learning framework, facing two potential limitations. First, in news encoder, single candidate news encoding suffers from an insufficient semantic information problem. Second, existing graph-based NR methods are promising but lack effective news-user feature interaction, rendering the graph-based recommendation suboptimal. To overcome these limitations, we propose dual-interactive graph attention networks (DIGAT) consisting of news- and user-graph channels. In the news-graph channel, we enrich the semantics of single candidate news by incorporating the semantically relevant news information with a semantic-augmented graph (SAG). In the user-graph channel, multi-level user interests are represented with a news-topic graph. Most notably, we design a dual-graph interaction process to perform effective feature interaction between the news and user graphs, which facilitates accurate news-user representation matching. Experiment results on the benchmark dataset MIND show that DIGAT outperforms existing news recommendation methods. Further ablation studies and analyses validate the effectiveness of (1) semantic-augmented news graph modeling and (2) dual-graph interaction.
%R 10.18653/v1/2022.findings-emnlp.491
%U https://aclanthology.org/2022.findings-emnlp.491/
%U https://doi.org/10.18653/v1/2022.findings-emnlp.491
%P 6595-6607

Markdown (Informal)

[DIGAT: Modeling News Recommendation with Dual-Graph Interaction](https://aclanthology.org/2022.findings-emnlp.491/) (Mao et al., Findings 2022)

• DIGAT: Modeling News Recommendation with Dual-Graph Interaction (Mao et al., Findings 2022)

ACL

• Zhiming Mao, Jian Li, Hongru Wang, Xingshan Zeng, and Kam-Fai Wong. 2022. DIGAT: Modeling News Recommendation with Dual-Graph Interaction. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 6595–6607, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.