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With the development of the Web, users spend more time accessing information that they seek. As a result, recommendation systems have emerged to provide users with preferred contents by filtering abundant information, along with providing means of exposing search results to users more effectively. These recommendation systems operate based on the user reactions to items or on the various user or item features. It is known that recommendation results based on sparse datasets are less reliable because recommender systems operate according to user responses. Thus, we propose a method to improve the dataset sparsity and increase the accuracy of the prediction results by using item features with user responses. A method based on the content-based filtering concept is proposed to extract category rates from the user–item matrix according to the user preferences and to organize these into vectors. Thereafter, we present a method to filter the user–item matrix using the extracted vectors and to regenerate the input matrix for collaborative filtering (CF). We compare the prediction results of our approach and conventional CF using the mean absolute error and root mean square error. Moreover, we calculate the sparsity of the regenerated matrix and the existing input matrix, and demonstrate that the regenerated matrix is more dense than the existing one. By computing the Jaccard similarity between the item sets in the regenerated and existing matrices, we verify the matrix distinctions. The results of the proposed methods confirm that if the regenerated matrix is used as the CF input, a denser matrix with higher predictive accuracy can be constructed than when using conventional methods. The validity of the proposed method was verified by analyzing the effect of the input matrix composed of high average ratings on the CF prediction performance. The low sparsity and high prediction accuracy of the proposed method are verified by comparisons with the results by conventional methods. Improvements of approximately 16% based on K-nearest neighbor and 15% based on singular value decomposition, and a three times improvement in the sparsity based on regenerated and original matrices are obtained. We propose a matrix reconstruction method that can improve the performance of recommendations. [ABSTRACT FROM AUTHOR]

A distributed system guarantees the acceptance of Byzantine fault tolerance (BFT) for information transmission. Practical BFT (pBFT) and asynchronous BFT (aBFT) are among the various available forms of BFT. Distributed systems generally share information with all participating nodes. After information is shared, the systems reshare it. Thus, ensuring BFT consumes a considerable amount of time. Herein, we propose Decision search protocols that apply the gossip protocol, denoted by DecisionBFT, for distributed networks with guaranteed BFT. Each protocol in DecisionBFT is completely asynchronous and leaderless; it has an eventual consensus but no round-robin or proof-of-work. The core concept of the proposed technology is the consensus structure, which is based on the directed acyclic graph (DAG) and gossip protocol. In the most general form, each node in the algorithm has a set of k neighbors of most preference. When receiving transactions, a node creates and connects an event block with all its neighbors. Each event block is signed by the hashes of the creating node and its k peers. The consensus structure of the event blocks utilizes a DAG, which guarantees aBFT. The proposed framework uses Lamport timestamps and concurrent common knowledge. Further, an example of a Decision search algorithm, based on the gossip protocol DecisionBFT, is presented. The proposed DecisionBFT protocol can reach a consensus when 2/3 of all participants agree to an event block without any additional communication overhead. The DecisionBFT protocol relies on a cost function to identify the k peers and generate the DAG-based consensus structure. By creating a dynamic flag table that stores connection information between blocks, the gossip protocol achieves a consensus in fewer steps than that in the case of the existing aBFT protocol. [ABSTRACT FROM AUTHOR]