Graph Neural News Recommendation with Long-term and Short-term Interest Modeling
Table 2: Comparison of Different Models
['Model', 'Adressa-1week AUC(%)', 'Adressa-1week F1(%)', 'Adressa-10week AUC(%)', 'Adressa-10week F1(%)']
We attribute the significant superiority of our model to its three advantages: (1) Our model constructs a heterogeneous user-news-topic graph and learns better user and news embeddings with high-order information encoded by GNN. (2) Our model considers not only the long-term user interest but also the short-term interest. (3) The topic information incorporated in the heterogeneous graph can help better reflect a user’s interest and alleviate the sparsity issue of user-item interactions. The news items with few user clicks can still aggregate neighboring information through the topics.