Enhanced Collaborative Filtering Through User-Item Subgroups, Particle Swarm Optimization and Fuzzy C-Means

Recommender systems are information filtering systems that assist users to retrieve relevant information from massive amounts of data. Collaborative filtering (CF) is the most widely used technique in recommender systems for predicting the interests of a user on particular items. In traditional CF preferences of all items from many users are collected in the prediction process and this may include items that are irrelevant to the active user (the user for whom the prediction is for). Recently, subgroup based methods have emerged which take into account correlation of users and a set of items to rule out consideration of superfluous items. In this paper our objective is to explore CF that considers only user-item subgroups which consist of only similar subset of users based on a subset of items. We propose a novel hybrid framework based on Particle Swarm Optimization and Fuzzy C-Means clustering that optimizes the searching behaviour of user-item subgroups in CF. The proposed algorithm is experimented and compared with several state-of-the-art algorithms using benchmark datasets. Accuracy metrics such as precision, recall and mean average precision is used to find the top N recommended items.