EEG-based emotion recognition has attracted substantial attention from researchers due to its extensive application prospects, and substantial progress has been made in feature extraction and classification modelling from EEG data. However, insufficient high-quality training data are available for building EEG-based emotion recognition models via machine learning or deep learning methods. The artificial generation of high-quality data is an effective approach for overcoming this problem. In this paper, a multi-generator conditional Wasserstein GAN method is proposed for the generation of high-quality artificial that covers a more comprehensive distribution of real data through the use of various generators. Experimental results demonstrate that the artificial data that are generated by the proposed model can effectively improve the performance of emotion classification models that are based on EEG.

As an important component to promote the development of affective brain–computer interfaces, the study of emotion recognition based on electroencephalography (EEG) has encountered a difficult challenge; the distribution of EEG data changes among different subjects and at different time periods. Domain adaptation methods can effectively alleviate the generalization problem of EEG emotion recognition models. However, most of them treat multiple source domains, with significantly different distributions, as one single source domain, and only adapt the cross-domain marginal distribution while ignoring the joint distribution difference between the domains. To gain the advantages of multiple source distributions, and better match the distributions of the source and target domains, this paper proposes a novel multi-source joint domain adaptation (MSJDA) network. We first map all domains to a shared feature space and then align the joint distributions of the further extracted private representations and the corresponding classification predictions for each pair of source and target domains. Extensive cross-subject and cross-session experiments on the benchmark dataset, SEED, demonstrate the effectiveness of the proposed model, where more significant classification results are obtained on the more difficult cross-subject emotion recognition task.

Recommender systems are an effective way to address the information overload problem. Due to the rapid development of deep learning, recommender systems based on deep learning can efficiently process massive training samples and integrate a variety of additional information, which can alleviate the inherent data sparsity and cold start problems of recommender systems. However, deep learning recommender systems need to collect massive amounts of user data for training models, which poses challenges for data security and user privacy protection. By using users’ local, not centrally collected, data, federated learning can collaboratively train deep learning recommendation models and improve the accuracy of recommendations without compromising user privacy. In this paper, we propose FedDeepFM, a deep recommender system model based on federated learning. First, FedDeepFM follows the standard paradigm of federated learning by uploading each client’s model parameters instead of the original data for model updates, according to the data processing inequality, which ensures that the server cannot collect users’ private information directly but at the same time is able to use the private data held by each client to jointly train a global model with generalization capabilities. Second, to prevent the server from learning users’ private information through indirect inference methods, a pseudo-interaction filling method is used to expand the client data to mask the real user data. The experimental results show that FedDeepFM, the deep recommender system model based on federated learning proposed in this paper, can provide high-quality recommendations while enhancing users’ privacy.

To comply with the rapid development of big data in mobile services, an increasing number of websites have begun to provide users with recommendation decisions in various areas, like shopping, tourism, food, and medical treatment. However, there are still some challenges in the field of medical recommendation systems, such as the lack of personalized medical recommendations and the problem of data sparseness, which seriously restricts the effectiveness of such recommendations. In this paper, we propose a personalized medical recommendation method based on a convolutional neural network that integrates revised ratings and review text, called revised rating and review based on a convolutional neural network (RR&R‐CNN). First, the review text is divided into user and doctor datasets, and BERT vectorized representations are performed on them. Moreover, the original rating features are revised by adding the sentiment analysis values of the review text. Then, the vectorized review text and the revised rating features are spliced together and input into the convolutional neural network to extract the deep nonlinear feature vectors of both users and doctors. Finally, we use a factorization machine for feature interaction. We conduct comparison experiments based on a Yelp dataset in the “Health & Medical” category. The experimental results confirm the conclusion that RR&R‐CNN has a better effect compared to a traditional method.

With the development of social networks and online mobile communities, group recommendation systems support users’ interaction with similar interests or purposes with others. We often provide some advices to the close friends, such as listening to favorite music and sharing favorite dishes. However, users’ personalities have been ignored by the traditional group recommendation systems while the majority is satisfied. In this paper, a method of group recommendation based on external social‐trust networks is proposed, which builds a group profile by analyzing not only users’ preferences, but also the social relationships between members inside and outside of the group. We employ the users’ degree of disagreement to adjust group preference rating by external information of social‐trust network. Moreover, having a discussion about different social network utilization ratio, we proposed a method to work for smaller group size. The experimental results show that the proposed method has consistently higher precision and leads to satisfactory recommendations for groups.