Hierarchical PSO clustering based recommender system

Due to a marked increase in the number of web users and their activities, many application areas that use patterns generated from their activities has been proposed. Web-based implicit recommender systems are one such application. An implicit recommender system is a tool that helps guide a user to a particular web resource based on implicit data. Implicit data comes from the web users activities without their active participation. Building such a system is a complex process due to two reasons, there is a huge amount of data and the quality of the data is poor. In this research, we tackle the first problem of generating patterns efficiently for recommender system by proposing Hierarchical Particle Swarm Optimization based clustering (HPSO-clustering). HPSO-clustering is a clustering approach based on Particle Swarm Optimization which combines both the properties of hierarchical and partitional clustering. We grouped the users´ session into different clusters. Recommendations for an active user are generated from these clusters. In this paper we report the results of accuracy of recommendations. We achieved an overall 60% to 65% of precision for an active user, while in some clusters the precision achieved was 100% when top 5 ranked recommendations were selected.