Recommendation systems alleviate the problem of information overload by helping users find information relevant to their preference. Memory-based recommender systems use correlation-based similarity to measure the common interest among users. The trust between users is often used to address the issues associated with correlation-based similarity measures. However, in most applications, the trust relationships between users are not available. A popular method to extract the implicit trust relationship between users employs prediction accuracy. This method has several problems such as high computational cost and data sparsity. In this paper, addressing the problems associated with prediction accuracy-based trust extraction methods, we proposed a novel trust-based method called AgreeRelTrust. Unlike accuracy-based methods, this method does not require the calculation of initial prediction and the trust relationship is more meaningful. The collective agreements between any two users and their relative activities are fused to obtain the trust relationship. To evaluate the usefulness of our method, we applied it to three public data sets and compared the prediction accuracy with well-known collaborative filtering methods. The experimental results show our method has large improvements over the other methods.
Improving network security is a difficult problem that requires balancing several goals, such as defense cost and need for network efficiency, in order to achieve proper results. In this paper, we devise method of modeling network attack in a zero-sum multi-objective game and attempt to find the best defense against such an attack. We combined Pareto optimization and Q-learning methods to determine the most harmful attacks and consequently to find the best defense against those attacks. The results should help network administrators in search of a hands-on method of improving network security.
Recommendation systems often use side information to both alleviate problems, such as the cold start problem and data sparsity, and increase prediction accuracy. One such piece of side information, which has been widely investigated in addressing such challenges, is trust. However, the difficulty in obtaining explicit relationship data has led researchers to infer trust values from other means such as the user-to-item relationship. This paper proposes a model to improve prediction accuracy by applying the trust relationship between the user and item ratings. Two approaches to implement trust into prediction are proposed: One involves the use of estimated trust, and the other involves the initial trust. The efficiency of the proposed method is verified by comparing the obtained results with four well-known methods, including the state-of-the-art deep learning-based method of neural graph collaborative filtering (NGCF). The experimental results demonstrate that the proposed method performs significantly better than the NGCF, and the three other matrix factorization methods, namely, the singular value decomposition (SVD), SVD++, and the social matrix factorization (SocialMF). Appl. Sci. 2019, 9, 4378 2 of 18 regularization factor is used to prevent overfitting. The singular value decomposition (SVD) is imposed as a baseline for model-based CF. However, MF models are difficult to generalize to the data when the original rating matrix is sparse. Several approaches have been proposed to address this challenge.The simplest and most common approach to resolve the sparsity problem involves inputting artificial data into the original matrix, and mean ratings are often used for this purpose. To address the problems associated with data sparsity in low-rank matrix approximation, Sebrero and Jaakkola [7] proposed a model that uses a weight inversely proportional to the noise variance. Similarly, Lee et al. [8] proposed an approach, namely, local low-rank matrix approximation (LLORMA), which approximates the observed matrix as the weighted sum of local low-rank matrices that are targeted to the local regions of the observed matrix. To construct the final approximated matrix, several local models are aggregated. Mackey et al. [9] proposed a similar method referred to as divide-and-conquer (DFC) MF. In contrast to LLORMA, DFC uses overlapping partitions of the observed matrix to construct the local models.To improve the efficiency of recommendation systems, the use of side information has been widely researched. Apart from side information such as the context and user and item characteristics, trust is often used as a reliable measure to incorporate into the CF technique. However, obtaining the explicit trust link between users is difficult. Here, we propose a new trust measure that can be derived directly from the user's preference matrix. In contrast to most trust metrics, which measure the degree of reliability between two users, our proposed metrics measure the user's trust regarding the preference they have expressed. T...
Using Pareto optimization in Multi-Objective Reinforcement Learning (MORL) leads to better learning results for network defense games. This is particularly useful for network security agents, who must often balance several goals when choosing what action to take in defense of a network. If the defender knows his preferred reward distribution, the advantages of Pareto optimization can be retained by using a scalarization algorithm prior to the implementation of the MORL. In this paper, we simulate a network defense scenario by creating a multi-objective zero-sum game and using Pareto optimization and MORL to determine optimal solutions and compare those solutions to different scalarization approaches. We build a Pareto Defense Strategy Selection Simulator (PDSSS) system for assisting network administrators on decision-making, specifically, on defense strategy selection, and the experiment results show that the Satisficing Trade-Off Method (STOM) scalarization approach performs better than linear scalarization or GUESS method. The results of this paper can aid network security agents attempting to find an optimal defense policy for network security games.
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