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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In order to solve the problem of information overload in big data era, the personalized recommender system has been widely used. Collaborative filtering, as a classical algorithm, has become the basis of the recommender system. In recent years, there are more and more recommender systems based on multiple data sources are proposed. Today's recommender systems integrate multiple data sources and recommendation methods are more accurate and explainable compare with rating-based recommendation systems. How to integrate multiple data sources to further improve the accuracy and interpretability of recommendation results, reduce computational complexity and cold start risk has become the key content of recommendation researches.