A Framework for Mobile Personalized-Based Recommender System Using Social Tag Clustering Approach

Whereas the social tag clustering approach has been widely used in various recommender systems, recent work has rarely shown the utilization of the tag clustering in the development of mobile recommender systems. In this study, we explore the work in mobile recommendation systems and propose a framework for developing a personalized-based recommendation system for mobile application, utilizing a social tag clustering approach. Additionally, we also design the interface on mobile and conduct an experiment with three fundamental pipelines. The results indicated that the similarity measure affects the recommendation results. This contribution, the proposed framework using a social tag clustering approach, will add the novelty to the research communities and be an alternative for developing future mobile recommendation systems.