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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]

Collaborative filtering (CF), one of the most famous methods for building recommendation systems, recommends relevant items to users or predicting ratings of users' unknown items. Matrix factorization (MF) models are well-known model to deal with predicting the rating problem. However, the recommendation system based on matrix factorization is hard to keep up with the rapidly changing real-world data. When ratings on new users or new items come, the static model can not fit well on new data. As a consequence, if the current thing does not apply, the prediction accuracy will lose. In addition, it is a significant computation cost to rebuild the model on the whole data. To capture these changes, in this paper, we construct an online-and-offline Collaborative Filtering with a multi-method model to improve the traditional CF method, called Online SGD with Offline Knowledge (OSGDO for short). Besides, we propose a real-time incremental recommendation framework on Apache Flink, which is a scalable stream and batch data processing platform. Meanwhile, we implement our proposed method on our proposed framework. Our method proves to be good at online training when new observations arrive. And the results of experiments show that the dynamic training process we proposed is more efficient than rebuild the model on all the data. At the same time, our algorithm performs well in practice and can achieve impressive accuracy quickly when it is tested with the well-known data sets of MoviesLens and Netflix. [ABSTRACT FROM AUTHOR]