Abstract: Recommender systems have become one of the necessary tools to help a web user find a potentially interesting resource based on their preferences. In implicit recommender systems, the recommendations are made based on the implicit information of the web users i.e. data collected from web logs or cookies without knowing users preferences. Developing such a recommender system is complex due to the huge amount of anonymous noisy data. In this paper we present a Particle Swarm Optimization (PSO) based clustering approach called Hierarchical Particle Swarm Optimization based clustering (HPSO-clustering) for building a recommender system based on implicit web usage data. The approach mimics multi-agent properties of the particles of a swarm and divide the problem space into smaller sub-spaces i.e. clusters. Each cluster represents a particular group of user with similar interests. Later, the K-nearest neighbours of the most relevant cluster are generated as recommendations for a web user and ranked based on their distance. We performed different experiments for preprocessing, to assess the quality of clusters, and for the accuracy of recommendations. An overall accuracy of 65% to 95% was achieved for different scenarios, while in some cases the accuracy touched 100 precent when the selection was made from the top-5 recommendations.
Keywords: Particle Swarm Optimization, data clustering, web-bots, web usage mining, evolutionary computation