Cross-domain recommendation is an effective technique to alleviate the data sparsity problem in recommender systems by utilizing the information from relevant domains. In this paper, we propose Crossdomain Deep Neural Network (CD-DNN) for the cross-domain recommendation. CD-DNN solves the rating prediction problem by modeling users and items using reviews and item metadata, which jointly learns features of users and items from not only the target domain but also other source domains. Latent factors for users and items are learned by several parallel neural networks, and the relevance of user features and item features is learned by maximizing prediction accuracy. CD-DNN builds a single mapping for user features in the latent space, so that the network for user is optimized together with item features from other domains. Experimental results indicate that the proposed CD-DNN significantly outperforms other state-of-the-art recommendation approaches on four public datasets of Amazon and it alleviates the data sparsity problem by leveraging more data across domains. INDEX TERMS Cross-domain recommendation, convolutional neural networks, rating prediction. NANNAN ZHENG received the Bachelor of Engineering degree from Xiamen University, China, where she is currently pursuing the degree with the Department of Automation. Her current research interests are in deep learning and recommendation systems at the System and Control Center Laboratory, Xiamen University. ZIANG XIONG received the Bachelor of Engineering degree from Xiamen University, China, in 2018, where he is currently pursuing the degree with the Department of Automation. His current research interests are in deep learning and recommendation systems at the System and Control Center Laboratory, Xiamen University. ZHIQIANG HU received the Bachelor of Engineering degree from the Chongqing University of Posts and Telecommunications, China. He is currently pursuing the degree with the Department of Automation, Xiamen University, China. His current research interests are in natural language processing and knowledge graph at the System and

As an important application of medical informatization, healthcare big data analysis has been extensively researched in the fields of intelligent consultation, disease diagnosis, intelligent question-answering doctors, and medical assistant decision support, and have made many achievements. In order to improve the comprehensiveness and pertinence of the medical examination, this paper intends to use healthcare big data analysis combined with deep learning technology to provide patients with potential diseases which is usually neglected for lacking of professional knowledge, so that patients can do targeted medical examinations to prevent health condition from getting worse. Inspired by the existing recommendation methods, this paper proposes a novel deep-learning-based hybrid recommendation algorithm, which is called medical-history-based potential disease prediction algorithm. The algorithm predicts the patient's possible disease based on the patient's medical history, providing a reference to patients and doctors to reduce the problem of delaying treatment due to unclear description of the symptom or limited professional knowledge. The experimental results show that our approach improves the accuracy of the potential diseases prediction.INDEX TERMS Healthcare big data, deep learning, recommendation, disease predicting.

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