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A recommendation System (RS) is an emerging technology to figure out the user's interests and intentions. As the amount of data increases exponentially, it is hard to analyze the user intentions and trigger the recommendation accordingly. In this research work, a novel recommendation system called the Deep Knowledge Graph based Attribute Preserving Recommendation (DKG-APR) is presented to analyze massive data and provide personalized recommendations to users. The Deep Knowledge Graph for Recommendation System (DKG-RS) uses Deep Convolutional Neural Network (DCNN) and attention mechanism to explicitly model high-order connections in knowledge graphs. According to empirical findings, Knowledge Graph Attention Network (KGAT) performs better than other state-of-the-art recommendation techniques like RippleNet and Neural FM. Additional research demonstrates the effectiveness of embedding propagation for high-order relation modeling and the advantages of the attention mechanism for interpretability.The results also show that user information is crucial in the recommendation system, as seen from the optimal node-drop-out ratio of 0.2, which led to the best recall value of 0.2 for all datasets. [ABSTRACT FROM AUTHOR]

Recent technological advancements have enabled users to conduct more sophisticated business transactions via Wi-Fi enabled networks. Typically, a compromised access point (CAP) can handle all traffic between a user and an Internet server, thus becoming a serious security hazard. In addition, an attacker can easily control the entire network using the CAP remotely and compromise as many victims as possible to form a botnet. This paper presents a hybrid recommendation prediction model for forecasting CAP attacks based on network traffic in a private network. This model combines various prediction techniques likethe time-series model, the kNN model and cross association algorithm for attack prediction. This hybrid blacklisting recommendation system effectively improves the prediction rate significantly as well as the robustness against poisoning attacks. [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]

The major challenge of recommendation system (RS) based on implict feedback is to accurately model users' preferences from their historical feedback. Nowadays, researchers has tried to apply adversarial technique in RS, which had presented successful results in various domains. To a certain extent, the use of adversarial technique improves the modeling of users' preferences. Nonetheless, there are still many problems to be solved, such as insufficient representation and low-level interaction. In this paper, we propose a recommendation algorithm NCGAN which combines neural collaborative filtering and generative adversarial network (GAN). We use the neural networks to extract users' non-linear characteristics. At the same time, we integrate the GAN framework to guide the recommendation model training. Among them, the generator aims to make user recommendations and the discriminator is equivalent to a measurement tool which could measure the distance between the generated distribution and users' ground distribution. Through comparison with other existing recommendation algorithms, our algorithm show better experimental performance in all indicators. [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]

Nowadays, we are living in an information overload age. A tremendous amount of information has been produced on the Internet, how to find the interesting information is the main goal of recommendation system research. However, the most of current traditional recommendation algorithms (such as Collaborative filtering) are suffering from flowing difficulties: (i) The traditional recommendation system assume that users are independent and identically distributed; this assumption fails to consider the social relation and connection between users, which is not consistent with the social relations in our real world. (ii) Although there are some recommendation system research began to focus on the trust relationship between users, trust information is also very sparse. This leads to most of datasets only contains very little information about the user's relationship. In this paper, we propose an innovative method that integrated users' trust propagation and singular value decomposition into recommendation Algorithm to improve the quality of the recommendation effectively and efficiently. We performed our experiments on two real data sets respectively, the public domain Epinions.com and Filemtrust.com. The experimental results show that our method has a better outperform. [ABSTRACT FROM AUTHOR]