Influence maximization is the problem of finding a set of k users in a social network, such that by targeting these k users one can maximize the spread of influence in the network. Recently a new type of social network has come into existence on platforms like Zomato and Yelp, where people can publish reviews of local businesses like restaurants, hotels, salons etc. Such social network can help owners of local businesses in making intelligent business decisions through the use of Targeted Marketing.
In this paper we present Spread Heuristic based Influence Maximization SHIM algorithm, our novel algorithm, which uses a heuristic approach that maximizes the influence spread every time a node is added to the set of influential nodes. In our work, we also introduce a new method to find information-propagation probability based on attributes of the user. We test the proposed algorithm on academic dataset of Yelp, and a comprehensive performance study shows that SHIM algorithm achieves greater Influence Spread than several other algorithms.
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