# News Recommendation Method Based on Candidate-Aware Long- and Short-Term Preference Modeling
## 1 Introduction
Therefore, dynamically adjusting the weights of different short-term preferences of users according to the candidate news helps the model to more accurately determine which candidate news the user will click on. In this regard, a novel method for news recommendation is proposed, we named it NRCLS. It is worth mentioning that, in our method, candidate news are optimized in different ways for different user preferences. We first construct a clicked news-candidate news graph and a user-clicked news heterogeneous graph. To allow for long-term user preference modeling to exploit both high-order structural information in graphs and potential relationships between users and candidate news, Graph attention networks (GATs) are applied to propagate embeddings sequentially over the two graphs. For short-term  
Figure 1: An example used to illustrate high-order structural information in a user sequence. (**a**) User sequence; (**b**) high-order information in user sequence. user preferences, a candidate-aware attention network that incorporates news subtopics is designed to obtain a fine-grained representation of short-term user preferences that consider the dynamic effect of candidate news on short-term user preference. The user representation is a concatenation of long- and short-term preferences. We then use the final representation to predict which candidate news the user will click on or not. In addition, we summarize the research gaps, research hypotheses and our research goals in Table 1. Here are our contributions:  
1. A Candidate-Enhanced graph attention network (Candidate-Enhanced GAT) is designed to propagate embeddings sequentially across the clicked news-candidate news graph and the user-clicked news heterogeneous graph, combining high-order structural information in the graph and the potential relationship between users and candidate news to model users' long-term preferences. 2. We design a candidate-aware attention network that incorporates news subtopics while modeling users' short-term preferences by taking into account the fine-grained variation of users' short-term preferences and the dynamic impact of candidate news on users' short-term preferences. 3. Our proposed method achieves superior recommendation performance on two real-world datasets. At the same time, we also perform several ablation studies. The results of the studies proved how effective the core modules of our method are. The remainder is organized as follows: Section 2 presents related works on news recommendation and graph neural networks. Section 3 provides a detailed description of our proposed method from four perspectives. Section 4 presents relevant experiments and ablation studies. In Section 5, we give a case study of the user. In Section 6, the shortcomings of our method and possible future research directions are discussed. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline Research hypotheses & Our study is based on two hypotheses: 1. high-order structural information in user sequences improves the quality of user preferences; 2. candidate news has a positive effect on users’ long- and short-term preferences \\ \hline Research gaps & Existing news recommendation methods have gaps that make it difficult to accurately match users’ long- and short-term preferences with candidate news items \\ \hline Research goals & High-quality recommendations are also realized by taking into account the impact of candidate news on users’ long-term and short-term preferences \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Research hypotheses, gaps, and goals. Figure 2: Some candidate news and recent news viewed by the user.