  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.
