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Collaborative filtering (CF) is the most successful approach of Recommender Systems that has been applied in a wide range of applications. In this approach, historical rating data is exploited to calculate similarity between pairs of users and predict user preference over unseen items. The similarity is calculated using users' global preferences estimated from their previous interactions with items. To this end, commonly rated items by a given pair of users are utilized to calculate the similarity. In a sparse dataset, there are limited co-rated items. In addition, inconsistent behaviours of users in rating items may add some noise to data, which makes the ratings more untrustworthy leading to less accuraterecommendations. In this manuscript, we introduce an individual ratings confidence measure (IRC) to calculate the confidence of a given rating to each item by the target user. IRC consists of 5 factors that help to have more information about the interest and boredom of users. User ratings are denoised by prioritizing extreme and high-confidence ratings, and finally, the denoised ratings are used to obtain similarity values. This approach leads to a more accurate neighbourhood selection and rating prediction. We show the superiority of the proposed method when compared with state-of-the-art recommender algorithms over some well-known benchmark datasets. [ABSTRACT FROM AUTHOR]

Tools available in graph theory have been recently applied to signals recorded from the human brain, where its cognitive functions are linked to topological properties of connectivity networks. In this work, we consider resting-state electroencephalography (EEG) signals recorded from healthy subjects and patients suffering from Alzheimer's disease (AD) in two conditions: eyes-open and eyes-closed. The EEGs are used to construct functional brain networks in which the nodes are EEG sensor locations and edges represent functional connectivity between them. The networks are then tested for a number of neurobiologically relevant graph theory metrics. The analyses show that the network properties are stable across all conventional frequency bands. AD brains in eyes-closed condition show significantly reduced local efficiency and modularity measures ( P < 0.05; Wilcoxon's ranksum test). We then use the network metrics as features for discriminating AD from healthy controls. Three feature selection methods (Genetic Algorithms (GA), Binary Particle Swarm Optimization (BPSO) and Social Impact Theory based Optimization (SITO)) are used to select the best feature set. GA with support vector machines (as classifier) results in an accuracy of 83% in eyes-close beta band. The set of optimal features include edge betweenness centrality, global efficiency, modularity and synchronizability. [ABSTRACT FROM AUTHOR]

Ever increasing popularity social networks has attracted many companies to use them for viral marketing. Identification of influential users for spreading news and marketing is a major challenge in viral marketing. In most of the existing influence maximization approaches, the topological locations of nodes on the network has been considered as a criterion to determine their influentiality. They have mainly neglected users' interest in the marketing message. Although a number of existing works have considered interests of users, they have not used any criterion to specify the interest. This manuscript proposes a novel criterion to measure the interest of users in the marketing messages. We then propose a novel algorithm to obtain the set of the most influential users. Experimental results on real-world and synthetic networks reveal effectiveness of the proposed method as compared to the existing state-of-the-art influence maximization algorithms. [ABSTRACT FROM AUTHOR]

Artificial Bee Colony (ABC) is an effective swarm optimization method featured with higher global search ability, less control parameters and easier implementation compared to other population-based optimization methods. Although ABC works well at exploration, its main drawback is poor exploitation affecting the convergence speed in some cases. In this paper, an efficient ABC-based optimization method is proposed to deal with high dimensional optimization tasks. The proposed method performs two modifications to the original ABC in order to improve its performance. First, it employs a chaos system to generate initial individuals, which are fully diversified in the search space. A chaos-based search method is used to find new solutions during ABC search process to enhance the exploitation capability of the algorithm and avoid premature convergence. Second, it incorporates a new search mechanism to improve the exploration ability of ABC. Experimental results performed on benchmark functions reveals superiority of the proposed method over state-of-the-art methods. [ABSTRACT FROM AUTHOR]