With the development of e-commerce, a large amount of personalized information is produced daily. To utilize diverse personalized information to improve recommendation accuracy, we propose a hybrid recommendation model based on users' ratings, reviews, and social data. Our model consists of six steps, review transformation, feature generation, community detection, model training, feature blending, and prediction and evaluation. Three groups of experiments are performed in this paper. Experiments A are used to identify the regression algorithm used in our model, Experiments B are used to identify the model to analyze review texts and the algorithm to detect social communities, and Experiments C compare our hybrid recommendation model with conventional recommendation models, such as probabilistic matrix factorization, UserKNN, ItemKNN, and social network-based models, such as socialMF and TrustSVD. The experiment results show that recommendation accuracy can be improved significantly with our hybrid model based on review texts and social communities.INDEX TERMS Recommender systems, social communities, review texts, ratings.

With the rapid growth of multimodal data, the cross-modal search has widely attracted research interests. Due to its efficiency on storage and computing, hashing-based methods are broadly used for large scale cross-modal retrieval. Most existing hashing methods are designed based on binary supervision, which transforms complex relationships of multi-label data into simple similar or dissimilar. However, few methods have explored the rich semantic information implicit in multi-label data to improve the accuracy of searching results. In this paper, the multi-level semantic supervision generating approach is proposed by exploring the label relevance. And a deep hashing framework is designed for multi-label image-text cross retrieval tasks. It can simultaneously capture the binary similarity and the complex multi-level semantic structure of data in different forms. Moreover, the effects of three different convolutional neural networks, CNN-F, VGG-16, and ResNet-50, on the retrieval results are compared. The experimental results on an open source cross-modal dataset show that our approach outperforms several state-of-the-art hashing methods, and the retrieval result on the CNN-F network is better than VGG-16 and ResNet-50. INDEX TERMS Cross-modal retrieval, deep learning, hashing method, multi-label learning.

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This paper presents a feature-based solution for 3D registration of CT and MRI images of a human knee. It facilitates constructing high-quality models with clear outlining of bone tissues and detailed illustration of soft tissues. The model will be used for analysing the effect of posterior cruciate ligament and anterior cruciate ligament deficiency. The solution consists of preprocessing, feature extraction, transformation parameter estimation and resampling, and blending. In preprocessing, we propose partial preserving and iterative neighbour comparing filtering to help segment bone tissues from MRI images without having to construct a statistical model. Through analysing the characteristics of knee images, tibia and femur are selected as the features and the algorithm for effectively extracting them is described. To estimate transformation parameters, we propose a method based on the statistical information of projected feature images, including translating according to the project feature image centroids and calculating the rotation angle by searching and mapping boundary points. We transform the MRI image and then blend it with the CT image by taking the maximum

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