DIGAT : Modeling News Recommendation with Dual-Graph Interaction (2022)

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.

## introduction news recommendation is an important technique to provide people with the news which satisfies their personalized reading interests @xcite . effective news recommender systems require both accurate textual modeling on news content @xcite @xcite and personal-interest modeling on user behavior @xcite . hence, most news recommendation methods (an et al., 2019; wu et al., 2019a,b,c,d; ge et al., 2020; 1 our code is available at https://github.com/veasonsilverbullet/digat . jian li is the corresponding author. qi et al., 2021b,c) adopt a news-user representation learning framework to learn discriminative news and user representations, as illustrated in figure 1 . though promising, there are still two potential limitations in the existing news recommendation framework. first, in news encoder, single candidate news encoding suffers from an insufficient semantic information problem. unlike long-term items in common recommendation (e.g., e-commerce product recommendation), the candidate news items are short-term and suffer from the cold start problem. in the real-world setting, news recommender systems usually handle the latest news, where existing user-click interactions are always not available foot_0 . hence, it is intractable to use existing user-click records to enrich the information of candidate news. on the other hand, compared to abundant historical clicked news in user encoder, the single candidate news may not contain sufficient semantic information for accurate news-user representation matching in the click prediction stage. prior studies @xcite pointed out that users were usually interested in specific news topics (e.g., sports). empirically, the text of single candidate news does not contain enough syntactic and semantic information to accurately represent a genre of news topic and match user interests. second, previous studies generally follow two research directions to model user history, i.e., sequence and graph modeling. formulating user history as a sequence of user's clicked news is a more prevalent direction, based on which time-sequential models @xcite @xcite and attentive models @xcite @xcite are proposed. besides, graph modeling is proved effective for recommender systems @xcite . @xcite and @xcite formulate news and users jointly in a bipartite graph to model newsuser interaction. however, since most candidate news in test data has no existing interaction with users (i.e., cold-news), the isolated cold-news nodes cause this bipartite graph modeling degenerate. recent works formulate user history as heterogeneous graphs and employ advanced graph learning methods to extract the user-graph representations @xcite @xcite . these works focus on how to extract fine-grained representations from the user-graph side but neglect necessary feature interaction between the candidate news and user-graphs. in this work, we propose dual-interactive graph attention networks (digat) to address the aforementioned limitations. digat consists of newsand user-graph channels to encode the candidate news and user history, respectively. in the newsgraph channel, we introduce semantic-augmented graph (sag) modeling to enrich the semantic representation of the single candidate news. in sag, the original candidate news is regarded as the root node, while the semantic-relevant news documents are represented as the extended nodes to augment the semantics of the candidate news. we integrate the local and global contexts of sag as the semanticaugmented candidate news representations. in the user-graph channel, motivated by @xcite and @xcite , we model user history with a news-topic graph to represent multilevels of user interests. most notably, we design a dual-graph interaction process to learn news-and user-graph representations with effective feature interaction. different from the individual graph attention network @xcite , digat updates news and user graph embeddings with the interactive attention mechanism. particularly, in each layer of the dual-graph, the user (news) graph context is incorporated into its dual news (user) node embedding learning iteratively. extensive experiments on the benchmark dataset mind @xcite show that digat significantly outperforms the existing news recommendation methods. further ablation studies and analyses confirm that semantic-augmented news graph modeling and dual-graph interaction can substantially improve news recommendation performance. ## related work personalized news recommendation is important to online news services @xcite . existing neural news recommendation methods typically aim to learn informative news and user representations @xcite @xcite @xcite @xcite @xcite . for example, @xcite used a cnn network to extract textual representations from news titles and used a gru network to learn short-term user interests combined with long-term user embeddings. the matching probabilities between candidate news and users are computed over the learned news and user representations. @xcite utilized multihead self-attention networks to learn informative news and user representations from news titles and user clicked history. these methods regarded the single candidate news as the input to news encoder, which may not contain sufficient semantics to represent a user-interested news topic. different from these methods, we encode the candidate news with semantic-augmented graphs to enrich its semantic representations. more recently, graph-based methods were proposed for news recommendation @xcite @xcite . for example, @xcite proposed a heterogeneous graph pooling method to learn fine-grained user representations. however, feature interaction between candidate news and users is inadequate or neglected in these methods. in contrast, our approach models effective feature interaction between news and user graphs for accurate news-user representation matching. ## conclusion in this work, we present a dual-graph interaction framework for news recommendation. in our approach, a graph enhanced semantic-augmentation strategy is employed to enrich the semantic information of candidate news. moreover, we design a dual-graph interaction mechanism to achieve ef-fective feature interaction between news and user graphs, facilitating more accurate news and user representation matching. our approach advances the state-of-the-art news recommendation methods on the benchmark dataset mind. extensive experiments and further analyses validate that sag modeling and dual-graph interaction can effectively improve news recommendation performance.