  News representation and user-oriented modeling are both essential for news
recommendation. Most existing methods are based on textual information but
ignore the visual information and users' dynamic interests. However, compared
to textual only content, multimodal semantics is beneficial for enhancing the
comprehension of users' temporal and long-lasting interests. In our work, we
propose a vision-linguistics coordinate time sequence news recommendation.
Firstly, a pretrained multimodal encoder is applied to embed images and texts
into the same feature space. Then the self-attention network is used to learn
the chronological sequence. Additionally, an attentional GRU network is
proposed to model user preference in terms of time adequately. Finally, the
click history and user representation are embedded to calculate the ranking
scores for candidate news. Furthermore, we also construct a large scale
multimodal news recommendation dataset V-MIND. Experimental results show that
our model outperforms baselines and achieves SOTA on our independently
constructed dataset.
