# News Recommendation Method Based on Candidate-Aware Long- and Short-Term Preference Modeling
## 1 Introduction  
Nowadays, people prefer reading news through online platforms, such as Microsoft Edge [1, 2]. However, with a large number of news releases daily, platforms have a hard time accurately locating the news that users are interested in [3]. Personalized recommendation, as a mature information filtering technology, is crucial to alleviate information overload. With its great advantages in profiling and improving user reading experience, personalized recommendations are receiving more and more attention from various fields [4, 5, 6, 7, 8]. There are typically three steps involved in personalized news recommendation: news feature extraction, user preference modeling, and click prediction. User preference modeling is one of the most crucial steps. Long-term user preferences reflect users' continuous features, while short-term user preferences reflect users' recent interests, and both are important for predicting users' next clicks. Therefore, personalized news recommendations need to consider both long-term and short-term user preferences so that user preferences can be better analyzed to provide accurate recommendations. Existing news recommendation methods for modeling long-term user preferences are usually based only on the news they have browsed. However, basing this only on the history of news browsed by users completely ignores the high-order structural information [9] among users. In the graph structure, the relationship between nodes can be arbitrary, and any two data elements in thegraph may be related to each other. High-order structural information is used to describe the complex connections between nodes on the way. As shown in Figure 1, \(u_{1}\) has direct interactions with \(n_{1}\), \(n_{3}\), \(n_{6}\), \(n_{5}\), and \(n_{7}\), and also establishes high-order interactions with \(u_{2}\) and \(u_{3}\) through the common interaction terms \(n_{1}\), \(n_{3}\), \(n_{6}\), and \(n_{7}\). Similarly, high-order interactions are established between \(n_{1}\) and \(n_{9}\) through the user \(u_{3}\). High-order structural information in the graph can help the model better mine users' long-term preference features which are mostly ignored by existing news recommendation methods. Short-term user preferences are an essential part of user preferences. Existing news recommendation methods for modeling short-term user preferences often obtain a representation of users' short-term preferences by analyzing the news they have recently viewed. However, users' short-term preferences usually change frequently [10], and users tend to read news in particular areas at particular times [11]. Existing news recommendation methods tend to ignore this fine-grained variation, which leads to an inaccurate modeling of short-term user preferences. The following example illustrates our point well. Our random sample of example users from the real-world dataset in Figure 2 has recently clicked more news about the NBA, which indicates that the user has a strong interest in basketball; however, the user also clicked on a news item about soccer. It is probably because the short-term user preferences changed subtly during the World Cup when the example user became briefly interested in soccer. By analyzing the distribution of subtopics of news recently clicked by the example user in Figure 2, the example user recently had more interest in basketball than soccer. However, the second candidate news only meets the user's interest in basketball, and is a poor match to the user's interest in soccer. In the news recommendation domain, candidate awareness refers to the consideration of the impact of candidate news information on user interest modeling during recommendation system design and user modeling. If short-term user preferences and candidate news are modeled independently, without considering the dynamic impact of candidate news on fine-grained short-term user preferences, the model may not recommend the news.