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Vol.26 No.1
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A Study on Personalized Recommendation Method Based on Contents Using Activity and Location Information

Journal of the Korean Society for Information Management / Journal of the Korean Society for Information Management, (P)1013-0799; (E)2586-2073
2009, v.26 no.1, pp.81-105
https://doi.org/10.3743/KOSIM.2009.26.1.081




& (2009). A Study on Personalized Recommendation Method Based on Contents Using Activity and Location Information. Journal of the Korean Society for Information Management, 26(1), 81-105, https://doi.org/10.3743/KOSIM.2009.26.1.081
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Abstract

In this paper, we propose user contents using behavior and location information on contents on various channels, such as web, IPTV, for contents distribution. With methods to build user and contents profiles, contents using behavior as an implicit user feedback was applied into machine learning procedure for updating user profiles and contents preference. In machine learning procedure, contents-based and collaborative filtering methods were used to analyze user's contents preference. This study proposes contents location information on web sites for final recommendation contents as well. Finally, we refer to a generalized recommender system for personalization. With those methods, more effective and accurate recommendation service can be possible.

keywords
IPTV, personalization, recommendation, collaborative filtering, contents-based recommendation, 개인화, 추천, 협업여과추천, 내용기반추천

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