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Recommender system design using movie genre similarity and preferred genres in SmartPhone

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Abstract

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.

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Acknowledgments

This work was supported by the Korea Science and Engineering Foundation (KOSEF) grant funded by the Korean government (MEST) (No. 2010-0000487)

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Authors and Affiliations

  1. Department of IT App. Tech. GSV, Hoseo University, Seoul, South Korea

    Kyung-Rog Kim & NamMee Moon

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  1. Kyung-Rog Kim
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  2. NamMee Moon
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Correspondence to NamMee Moon.

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Kim, KR., Moon, N. Recommender system design using movie genre similarity and preferred genres in SmartPhone. Multimed Tools Appl 61, 87–104 (2012). https://doi.org/10.1007/s11042-011-0728-y

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