Accuracy improvement has been one of the most outstanding issues in the recommender systems research community. Recently, multi-criteria recommender systems that use multiple criteria ratings to estimate overall rating have been receiving considerable attention within the recommender systems research domain. This paper proposes a neural network model for improving the prediction accuracy of mul...

We study the application of spectral clustering, prediction and visualization methods to graphs with negatively weighted edges. We show that several characteristic matrices of graphs can be extended to graphs with positively and negatively weighted edges, giving signed spectral clustering methods, signed graph kernels and network visualization methods that apply to signed graphs. In particular,...

The defining characteristics of the Internet today is an abundance of information and choice. Recommender Systems (RS), designed to alleviate this problem, have so far not been very successful, and recent research suggests that this is due to the lack of the social context and inter-personal trust. We simulated an online film RS with 60 participants, where recommender information was added to t...

In recent years, collaborative filtering (CF) methods are important and widely accepted techniques are available for recommender systems. One of these techniques is user based that produces useful recommendations based on the similarity by the ratings of likeminded users. However, these systems suffer from several inherent shortcomings such as data sparsity and cold start problems. With the dev...

Wind power generation has indicated an exponential increase during last two decades and existing transmission network infrastructure is increasingly becoming inadequate to transmit remotely generated wind power to load centres in the network. The dynamic line rating (DLR) is one of the viable solutions to improve the transmission line ampacity during high wind penetration without investing on a...