DIGAT: Modeling News Recommendation with Dual-Graph Interaction
- URL: http://arxiv.org/abs/2210.05196v1
- Date: Tue, 11 Oct 2022 07:01:40 GMT
- Title: DIGAT: Modeling News Recommendation with Dual-Graph Interaction
- Authors: Zhiming Mao, Jian Li, Hongru Wang, Xingshan Zeng, Kam-Fai Wong
- Abstract summary: News recommendation (NR) is essential for online news services.
Existing NR methods typically adopt a news-user representation learning framework, facing two potential limitations.
We propose dual-interactive graph attention networks (DIGAT) consisting of news- and user-graph channels.
- Score: 23.31021910348558
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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