M. Taherinia; M. Esmaeili; B. Minaei Bidgoli
Abstract
The Influence Maximization Problem in social networks aims to find a minimal set of individuals to produce the highest influence on other individuals in the network. In the last two decades, a lot of algorithms have been proposed to solve the time efficiency and effectiveness challenges of this NP-Hard ...
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The Influence Maximization Problem in social networks aims to find a minimal set of individuals to produce the highest influence on other individuals in the network. In the last two decades, a lot of algorithms have been proposed to solve the time efficiency and effectiveness challenges of this NP-Hard problem. Undoubtedly, the CELF algorithm (besides the naive greedy algorithm) has the highest effectiveness among them. Of course, the CELF algorithm is faster than the naive greedy algorithm (about 700 times). This superiority has led many researchers to make extensive use of the CELF algorithm in their innovative approaches. However, the main drawback of the CELF algorithm is the very long running time of its first iteration. Because it has to estimate the influence spread for all nodes by expensive Monte-Carlo simulations, similar to the naive greedy algorithm. In this paper, a heuristic approach is proposed, namely Optimized-CELF algorithm, to improve this drawback of the CELF algorithm by avoiding unnecessary Monte-Carlo simulations. It is found that the proposed algorithm reduces the CELF running time, and subsequently improves the time efficiency of other algorithms that employed the CELF as a base algorithm. Experimental results on the wide spectral of real datasets showed that the Optimized-CELF algorithm provided better running time gain, about 88-99% and 56-98% for k=1 and k=50, respectively, compared to the CELF algorithm without missing effectiveness.
H.3.15.1. Adaptive hypermedia
M. Tahmasebi; F. Fotouhi; M. Esmaeili
Abstract
Personalized recommenders have proved to be of use as a solution to reduce the information overload problem. Especially in Adaptive Hypermedia System, a recommender is the main module that delivers suitable learning objects to learners. Recommenders suffer from the cold-start and the sparsity ...
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Personalized recommenders have proved to be of use as a solution to reduce the information overload problem. Especially in Adaptive Hypermedia System, a recommender is the main module that delivers suitable learning objects to learners. Recommenders suffer from the cold-start and the sparsity problems. Furthermore, obtaining learner’s preferences is cumbersome. Most studies have only focused on similarity between the interest profile of a user and those of others. However, it can lead to the gray-sheep problem, in which users with consistently different opinions from the group do not benefit from this approach. On this basis, matching the learner’s learning style with the web page features and mining specific attributes is more desirable. The primary contribution of this research is to introduce a feature-based recommender system that delivers educational web pages according to the user's individual learning style. We propose an Educational Resource recommender system which interacts with the users based on their learning style and cognitive traits. The learning style determination is based on Felder-Silverman theory. Furthermore, we incorporate all explicit/implicit data features of a page and the elements contained in them that have an influence on the quality of recommendation and help the system make more effective recommendations.
