A Neural News Recommendation Based on Multi-aspect Article Representation

Neural network-based news recommendation methods can effectively personalize news recommendations to users, however, the features of news are not fully exploited in existing neural network-based recommendation methods. In order to extract highly abstract feature representations from news, a deep learning model based on multi-view representation (MUSA) is proposed. The model comprises two core components: a news encoder and a user interest encoder. In the news encoder, Transformer and word-level attention network are combined to learn the news representations from multiple views such as title, abstract, entity, category and sub-category, and five modules are used to extract the news information from each of the five views, and the representations obtained from each module are fused to obtain the final news features. In the user interest encoder, multi-head self-attention mechanisms and news-level attention networks are utilized to capture user interest preferences from their historical browsing records. Lastly, the model was compared with NPA, LSTUR, NRMS and other models on three real datasets in a comparative experiment; in order to explore the effect of each module in the news encoder on the model effect, ablation experiments were carried out; in order to explore the effect of the size of the experimental training dataset on the model effect, a training dataset size analysis experiments were conducted. The results of the comparison experiments show that the MUSA model outperforms the other baseline models in terms of performance on AUC, MRR, nDCG@ 5 and nDCG@ 10. The results of the ablation experiments show that the multi-view news coding approach is optimal. The training dataset size analysis experiments show better robustness of the MUSA model compared to the baseline model.