# News Recommendation Method Based on Candidate-Aware Long- and Short-Term Preference Modeling
## 2 Related Works  
### News Recommendation  
The existing news recommendation methods are mainly based on content and collaborative filtering to recommend the desired news for users. News recommendation methods based on collaborative filtering usually recommend news that users may be interested in based on the similarity between them [12]. However, collaborative filtering-based recommendation algorithms inevitably confront serious data sparsity problems caused by the increase in the number of users and news. Content-based news recommendation methods are centered on inferring the news that a user is likely to be interested in based on the news that the user has browsed, considering the actual content of the news. Recently, more and more content-based news recommendation methods have begun to utilize deep neural networks to exploit user representation with the development of deep learning. For example, [PERSON] et al. proposed a news recommendation method called DAN [13], which uses two parallel Convolutional Neural Networks (CNN) to extract news features and captures richer user sequence features with an Recurrent Neural Network (RNN) that incorporates attention. [PERSON] et al. [14] used the knowledge graph and enhanced news representation with information about the entities contained in the news in their method. What exists in common between the above two approaches is that they both apply the candidate-aware attention mechanism to model user preferences, which greatly contributes to the development of user preference modeling in the news recommendation domain.The news recommendation method called NAML [15] takes different elements of news as inputs to obtain news representations, and distinguishes the importance of different news according to additive attention networks to finally obtain the representation of the user. The method is innovative in that it introduces multi-perspective learning into news modeling, and provides direction for subsequent work. News clicks are also related to multiple factors, such as the location of the news. [PERSON] et al. proposed a method called DebiasRec [16] to learn unbiased user representations by modeling both news content and news bias information. Unlike the above methods for extracting coarse-grained user representations, [PERSON] et al. [17] proposes to model users at multiple levels and match them with candidate news at multiple levels, achieving better recommendation performance. A novel news recommendation method called MMNR [18], captures user interests at multiple levels, including word-level, news-level, and higher levels, and then learns multiple user interest vectors at each level. MMNR achieves superior recommendation performance than HieRec due to the involvement of word-level interests. [PERSON] et al. proposed an innovative news recommendation method called D-HAN [19], which focuses on solving the problem of existing methods failing to accurately capture and reflect the evolution of users' interests over time by means of a Dynamic Attention Mechanism (DAM). [PERSON] and [PERSON] have proposed a new neural network architecture called PMSN [20] for news recommendation. This model combines multi-head self-attention mechanisms and personalized attention mechanisms, optimizing from both the word and news levels. PMSN integrates various types of embeddings, including TF-IDF additive embeddings, word embeddings, and positional embeddings, to enrich the representation of words and make news recommendations location-aware. Furthermore, by embedding user IDs into the model, PMSN can highlight words and news that are rich in information related to users' personal interests, enhancing the interpretability of personalized recommendations. However, modeling users based only on what they have historically viewed completely ignores the high-order structural information in the graph. News is often clicked by different users, and through shared clicks, users are connected. Similarly, high-order links often exist between news and news.