# News Recommendation Method Based on Candidate-Aware Long- and Short-Term Preference Modeling
## 4 Experiments
### Ablation Studies
It shows that the click news-candidate news graph provides a news-to-news link which gives candidate news the ability to empower the representation of the click news. Exploiting this potential relationship enables higher quality long-term user preference modeling. (3) Our first hypothesis is correct and partially justifies the second. This is because whether Base replaces the original long-term preference modeling approach with GATs, or with Candidate-Enhanced GAT, the performance of the model improves to varying degrees. The reason that the second hypothesis cannot be fully justified is that this ablation study did not address short-term user preferences. Thirdly, we evaluate the effect of users' short-term preferences on model performance in this paper. To verify that short-term user preferences are effective for different models, we model user short-term preferences using a simple additive attention mechanism. We set up the following comparisons: (1) Base + Short: We introduce short-term preference modeling on the basis of Base mentioned above. (2) Base + Candi-Graph + Short: We introduce short-term user preference modeling while modeling long-term user preferences using Candidate-Enhanced GAT. We can draw the following conclusions from Figure 7: (1) Short-term user preferences are an important part of overall user preferences. We can clearly find that the performance of the model can be significantly improved, no matter whether the short-term user preference modeling is introduced into the model modeling long-term user preference with a self-attention mechanism or the modeling of long-term user preference is modeled by the Candidate-Enhanced GAT that we constructed. This suggests that it is one-sided to represent the overall user preference only by long-term user preference, and that better performance can be achieved by considering both short-term and long-term user preference. (2) Modeling short-term user preferences without considering candidate news and using only additive attention mechanisms can still achieve good performance. There is also a  
Figure 7: Ablation study on user preference encoder. (**a**) Ablation study on long-term user preference encoder; (**b**) ablation study on short-term user preference encoder. relatively important part of the user's recent browsing history, which can be effectively captured by the additive attention mechanism to achieve good recommendation performance. In addition, we focus on the effectiveness of candidate awareness networks incorporating news subtopics for modeling users' short-term preferences. (1) w/o FG: to test the effectiveness of extracting fine-grained features of short-term user preferences, we model short-term user preferences in a coarse-grained manner. (2) w/o Candidate: to evaluate the dynamic impact of introducing candidate news on short-term user preferences, we independently model short-term user preferences, ignoring the effect of candidate news on fine-grained short-term user preferences. From Figure 8, we can find that: (1) Candidate-aware attention networks that incorporate news subtopics are indispensable for model performance improvement. When we remove the candidate perceptual network that incorporates news subtopics and only use the additive attention mechanism to model short-term user preferences, the performance of the model undergoes a significant degradation. This suggests that mining the fine-grained features of users' short-term preferences while considering the effects of candidate news on different interests helps to model more accurate user interests. (2) Candidate news can improve the quality of short-term user preferences. When the model models user short-term preferences in a candidate-agnostic way, the model performance decreases, which suggests that candidate news has a positive impact on user short-term preferences.