# News Recommendation Method Based on Candidate-Aware Long- and Short-Term Preference Modeling
## 4 Experiments
### Ablation Studies  
We conducted several ablation experiments on the MIND-small dataset to verify the importance of the core module of the model for model performance. Firstly, to evaluate whether the news encoder in NRCLS has a positive impact on high-quality news modeling, the following comparisons are settled: (1) w/o abstract: we only use news titles, topics, and entities as inputs to the news encoder to evaluate the impact of news abstracts on the model's performance; and (2) w/o MHCA: we only model news titles and abstracts with an additive attention mechanism. From Figure 6, we can find that: (1) The information contained in the abstracts helps in modeling the news. When abstracts are removed and only titles, categories, and entities are used as inputs to the news encoder, the model's performance is then degraded. This shows that news abstracts also contain a lot of valid information, and exploiting this information contributes to high-quality news modeling. (2) MHCA contributes to the news encoder's better understanding of the whole news article. News titles and abstracts often contain some obscure words, and the MHCA helps the news encoder better understand these words by establishing a potential link between the title and the abstract, thus improving the model's performance. Secondly, we evaluate the effectiveness of Candidate-Enhanced GAT for modeling users' long-term preferences in this paper. This ablation experiment contains the following comparisons: (1) Base: To verify the importance of the Candidate-Enhanced GAT in modeling users' long-term preferences, we model users' long-term preferences with the self-attention mechanism instead of the Candidate-Enhanced graph attention network. (2) Base + Candi-Graph: Only the users' long-term preferences are used to represent the overall user preferences to evaluate the effectiveness of candidate awareness in modeling users' long-term preferences. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**MIND-Small1**} & \multicolumn{4}{c}{**MIND-Large**} \\ \cline{2-9}
**Model** & **AUC** & **MRR** & **NDCG@5** & **NDCG@10** & **AUC** & **MRR** & **NDCG@5** & **NDCG@10** \\ \hline LibFM & 57.83 & 26.96 & 27.93 & 34.74 & 60.12 & 28.43 & 30.16 & 36.10 \\ Wide Deep & 60.21 & 27.55 & 28.42 & 36.60 & 62.76 & 29.42 & 31.44 & 37.61 \\ ESNR & 62.14 & 28.17 & 30.45 & 36.91 & 64.32 & 31.29 & 33.67 & 40.31 \\ NPA & 63.56 & 29.31 & 32.13 & 38.27 & 66.56 & 31.70 & 34.86 & 40.68 \\ NAML & 64.25 & 29.80 & 32.56 & 39.15 & 66.77 & 31.84 & 35.16 & 41.53 \\ LSTUR & 65.23 & 30.40 & 33.92 & 39.82 & 67.83 & 32.54 & 35.96 & 42.29 \\ NRMS & 65.48 & 30.78 & 34.18 & 39.86 & 67.50 & 32.20 & 35.45 & 42.06 \\ FIM & 64.71 & 29.82 & 33.36 & 40.10 & 68.04 & 32.63 & 36.23 & 42.49 \\ UaG & 65.88 & 30.62 & 33.79 & 40.37 & 68.22 & 32.80 & 36.35 & 42.70 \\ AMM & 65.41 & 29.71 & 33.25 & 40.15 & 68.02 & 34.12 & 36.78 & 42.47 \\ \hline
**NRCLS** & **66.35** & **31.57** & **34.78** & **41.3** & **68.35** & **33.12** & **36.70** & **43.03** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of different baselines with our proposed method. Figure 6: Ablation study on news encoders. (3) Base + Graph: In order to evaluate the impact of candidate news on modeling user long-term preference, here the modeling of user long-term preference is performed by applying GATs only to the user-click news heterogeneous graph. From Figure 7, we can find that: (1) GATs help long-term user preference modeling, When replacing the self-attention mechanism with the GATs to model long-term user preferences, the performance of the model undergoes a significant improvement. This indicates that GATs can exploit the graph structure information that the self-attention mechanism cannot capture. (2) Candidate news plays a positive role in modeling long-term user preferences. The performance of NRCLS is remarkably improved when the clickable news-candidate news graph is added to the model.