Recommender system design using movie genre similarity and preferred genres in SmartPhone
As e-commerce (e.g. www.?amazon.?com) and social media (e.g. www.?facebook.?com) services evolve, studies of recommender systems advance, especially concerning the application of collective intelligence to personalized service. With the development of smartphones and the new mobile environment, studies of customized services increase despite the physical limitations of mobile devices. A typical example combines customized services with location-based services. In this study, we propose a recommender system using movie genre similarity and preferred genres. A movie genre similarity profile is designed and generated to provide related services in a mobile experimental environment before prototyping and testing with data from MovieLens. In order to accomplish this, genre similarity correlations are determined with a Pearson correlation coefficient, and similar clusters are derived. The correlations within clusters are used to define genre similarity. Genre similarity is then used to recommend new genres to targeted customers.
recommender system clustering smart-phone multi-property interactive service