Recommender System Using the Movie Genre Similarity in Mobile Service

Users of the various opinions and knowledge are generated and shared through the collective intelligence, a research on recommender systems are being continued at a variety of areas to use this at a personalized service. Also, despite the constraints of mobile device, personalized service is accelerating as development of the mobile environment. Therefore, we propose the recommender system using the genre similarity and preferred genre. After finding the relationship between genres by Pearson correlation coefficient, it produces a group by K-Means clustering. It creates a genre similarity profile by similar relationship between genres within a group. Suggest recommender system is reflected the genre similarity and preferred genre by target customer preferred genre. After designing and prototyping this to be able to be serviced at mobile experiment environment, it evaluates by applying to MovieLens Data set.