CNN with depthwise separable convolutions and combined kernels for rating prediction

Recently, deep learning based techniques exploiting reviews are extensively studied for rating prediction and result in good performance. Some studies consider word level review information along with attention mechanism to capture the most influential content, thus making the methods even complex. Deep neural networks with Depthwise Separable Convolutions have made significant progress in the area of image and video analysis. The success of these techniques encourages adopting them towards improvements in rating prediction using review text. In this paper, we present a novel CNN based architecture with Depthwise Separable Convolutions and Combined Kernels (CNN-DSCK) for rating prediction exploiting product reviews. In the proposed method, we use two parallel CNNs with Depthwise Separable Convolutions to extract semantic features from the text reviews of users and items using different kernels in parallel and then select the important information from these features through pooling. Finally concatenate the pooling information obtained from different kernels in each network. The features obtained through each network are then fused and the most relevant higher-order features are extracted through fully connected dense layer at the top of network. Extensive experiments on real-world datasets demonstrate that CNN-DSCK significantly outperforms state of the art baseline models.