An evolutionary clustering approach based on temporal aspects for context-aware service recommendation

Over the last years, recommendation techniques have emerged to cope with the challenging task of optimal service selection, and to help consumers satisfy their needs and preferences. However, most existing models on service recommendation only consider the traditional user-service relation, while in the real world, the perception and popularity of Web services may depend on several conditions including temporal, spatial and social constraints. Such additional factors in recommender systems influence users' preferences to a large extent. In this paper, we propose a context-aware Web service recommendation approach with a specific focus on time dimension. First, K-means clustering method is hybridized with a multi-population variant of the well-known Particle Swarm Optimization (PSO) in order to exclude the less similar users which share few common Web services with the active user in specific contexts. Slope One method is, then, applied to predict the missing ratings in the current context of user. Finally, a recommendation algorithm is proposed in order to return the top-rated services. Experimental studies confirmed the accuracy of our recommendation approach when compared to three existing solutions.