Personalized News Recommendation Based on the Text and Image Integration

The personalized news recommendation has been very popular in the news recommendation field. In most research, the picture information in the news is ignored, but the information conveyed to the users through pictures is more intuitive and more likely to affect the users' reading interests than the one in the textual form. Therefore, in this paper, a model that combines images and texts in the news is proposed. In this model, the new tags are extracted from the images and texts in the news, and based on these new tags, an adaptive tag (AT) algorithm is proposed. The AT algorithm selects the tags the user is interested in based on the user feedback. In particular, the AT algorithm can predict tags that a user may be interested in with the help of the tag correlation graph without any user feedback. The proposed AT algorithm is verified by experiments. The experimental results verified the AT algorithm regarding three evaluation indexes F1-score (F1), area under curve (AUC) and mean reciprocal rank (MRR). The recommended effect of the proposed algorithm is found to be better than those of the various baseline algorithms on real-world datasets.