A discrete genetic learning enabled PSO for targeted positive influence maximization in consumer review networks

Abstract

A consumer review network (CRN) is a social network among the members of a consumer review website where the relationships are formed based on the mutual ratings of the existing reviews of the participating members. The relationship is positive (negative) i.e. trust-based (distrust-based) when most of the reviews are given high (low) ratings. In a CRN, the consumers may not be interested in all product categories. The influence maximization in such a network demands seed set selection in such a way that the number of influenced consumers (with interest in the advertised product category) will be maximum. Formally, this is referred to as the targeted influence maximization (TIM) problem. Moreover, as the CRN is treated as a signed social network, the polarity of the social relationships impacts the influence propagation. As per the present state of the art, none of the existing solutions for TIM have considered the network as a signed one and are thus not suitable for CRN. In this paper, a metaheuristic discrete genetic-learning-enabled particle swarm optimization algorithm combined with a trustworthiness-heuristic-based local search strategy has been proposed for targeted positive influence maximization in CRNs. The existing spread estimation function has been replaced by a computationally efficient positive influence spread estimation function. The experiment has been conducted on two real-life CRNs and compared with the existing notable algorithm for necessary validation of the proposed solution.