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With the explosive increase of information, recommendation system is applied in a variety of areas. However, the performance of recommendation system is limited due to issues such as data sparsity, cold starts and poor semantic understanding. In order to make full use of external information to assist recommendation, deeply mine the semantic information of review text and further improve the performance of recommendation system, a deep recommendation system based on knowledge graph and review text (Drs-kgrt) is proposed in this paper. In Drs-kgrt, knowledge graph, review text and the social records between users are used as auxiliary information to improve recommendation performance. Firstly, the review text is divided into user review text and item review text. BERT (Bidirectional Encoder Representation from Transformers) is used to accurately understand semantic information in user review text and the social records between users. The trust relationship between users and user preferences are fully mined to form user feature vectors. Secondly, BERT and knowledge graph entity recognition link technology are combined to extract item attribute feature entities and their associated entities. The fine-grained features of the items are analyzed to form item feature vectors. Thirdly, based on the scoring matrix, latent vectors of users and items are obtained by matrix decomposition. The deep features of users and items are generated based on user feature vectors, item feature vectors, latent vectors of users and items, the deep recommendation system is established to predict user scores for items. Finally, experiments are conducted on the Douban dataset and Amazon Movie Review dataset, the results show that the proposed algorithm can achieve better performance compared with other benchmark recommendation algorithms. [ABSTRACT FROM AUTHOR]

Adding noise to user history data helps to protect user privacy in recommendation systems but affects the recommendation performance. To solve this problem, a matrix factorization tourism point of interest recommendation model based on interest offset and differential privacy is proposed in this paper. The recommendation performance of the model is improved by analyzing user interest preferences. Specifically, user interest offsets are extracted from user tags and user ratings under time-series factors to calculate user interest drift. Then, similar neighbors are found to train user feature preferences which are integrated into the matrix model in the form of regular terms. Meanwhile, based on the differential privacy theory, a privacy neighbor selection algorithm combining the K-Medoides clustering algorithm and index mechanism is designed to effectively protect the identity of neighbors and prevent KNN attacks. Besides, the Laplace mechanism is used to implement differential privacy protection for the model's gradient descent process. Finally, the feasibility of the proposed recommendation model is verified through experiments, and the experimental results indicate that this model has advantages in recommendation accuracy and privacy protection. [ABSTRACT FROM AUTHOR]

Tensors have been explored to share latent user-item relations and have been shown to be effective for recommendation. Tensors suffer from sparsity and cold start problems in real recommendation scenarios; therefore, researchers and engineers usually use matrix factorization to address these issues and improve the performance of recommender systems. In this paper, we propose matrix factorization completed multicontext data for tensor-enhanced algorithm a using matrix factorization combined with a multicontext data method for tensor-enhanced recommendation. To take advantage of existing user-item data, we add the context time and trust to enrich the interactive data via matrix factorization. In addition, Our approach is a high-dimensional tensor framework that further mines the latent relations from the user-item-trust-time tensor to improve recommendation performance. Through extensive experiments on real-world datasets, we demonstrated the superiority of our approach in predicting user preferences. This method is also shown to be able to maintain satisfactory performance even if user-item interactions are sparse. [ABSTRACT FROM AUTHOR]

With the popularity of networks and the increasing number of online users, recommender systems have suffered from the privacy leakage of sensitive information. While people enjoy recommender services, their information is exposed to the networks. To protect the privacy of users when using the recommender services, we propose a multi-level combined privacy-preserving model that maintains high accuracy of recommendation with privacy protection and alleviates the data sparsity problem. Our scheme contains two steps of recommendation. First, a multi-level combined random perturbation (MCRP) model is proposed on the client side. Our model dynamically divides multiple disturbance levels and adds noise of different ranges to the rating matrix according to Gaussian and uniform mixed disturbances. Second, on the server side, we propose a pseudo rating prediction filling (PRPF) algorithm based on the matrix factorization model. Combining the PRPF algorithm with the MCRP method significantly improves the recommender accuracy and effectively increases privacy security. Sensitive analysis and comparison experiments show that the proposed privacy method has certain advantages in security and recommender accuracy by using three publicly available datasets. [ABSTRACT FROM AUTHOR]

Rapid web growth and associated applications have proven of colossal importance for recommender systems. In the current digital world, a recommender system aims to acquire high-level prediction-based accuracy. However, many studies have suggested diversity-based recommendations are required for high-level accuracy. Group recommendation systems (GRS) recommend lists of items to a group of users according to their social activities, such as planning for a holiday tour, watching movies, etc. Using GRS, preferences/choices shared by users affected all the available aggregation with GRS leads to information loss and negatively affects 'diversity.' To handle the problem of 'information loss,' which is caused by aggregation, this paper proposes fuzzy-based GRS and argues that communicating such hesitant information will prove beneficial to generating recommendations. To find the valuable suggestions, greater focus must be dedicated to avoiding lack of variety and interest in the complete list of recommendations. In this article, we propose a novel Parallel Computing Group Recommendation System, which quantifies different approaches, chooses the right approach for group recommendation, and quickly generates optimal results. This proposed approach is an ensemble model of parallel ranking and matrix factorization that facilitates a diversified group recommendation list. Experimental evaluation signals that our model achieves higher diversity positively packed with user satisfaction. [ABSTRACT FROM AUTHOR]