Abstract: In the e-commerce era,online reviews are regarded as important product evaluations,which profoundly influence consumers' decision-making process.However,the exponentially increasing number of reviews and unstructured review data pose challenges to feature selection and accuracy improvement of review helpfulness prediction.In addition,current research mainly focuses on shallow features and feature extraction of review texts,the image information contained in review photos is often ignored.Besides,multi-modal information such as review text,photos,and shallow features needs to be refined and fused by app-lying multi-modal fusion methods.Based on these,this paper regards review photos and review text as a latent feature affecting the helpfulness of online reviews,and designs a shallow feature set according to the KAM knowledge adoption theory.For the data of three modalities,a deep prediction model,i.e.,three-modal review helpfulness prediction based on co-attention mechanism(TMCAM) is proposed,which can achieve the interaction and fusion of cross-modal information.The superior performance of the TMCAM model is tested through experiments,and it is proved that the complementation of image and text information can achieve better results than single modal information.Besides,shallow features can help predict the reviews helpfulness.Moreover,compared with simple modal features splicing,using collaborative attention mechanism for cross-modal information interaction helps to improve the perception of reviews helpfulness.

Key words: Review helpfulness, Co-attention mechanism, Multimodal fusion, Natural language processing, Deep learning