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Automatic and personalized recommendation of TV program contents using sequential pattern mining for smart TV user interaction

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Abstract

Due to the excessive number of TV program contents available at user’s side, efficient access to the preferred TV program content becomes a critical issue for smart TV user interaction. In this paper, we propose an automatic recommendation scheme of TV program contents in sequence using sequential pattern mining (SPM). Motivation of sequential TV program recommendation is based on TV viewer’s behaviors for watching multiple TV program contents in a row. A sequence of TV program contents for recommendation to a target user is constructed based on the features such as an occurrence and net occurrence of frequently watched TV program contents from the similar user group to which the target user belongs. Three types of SPM methods are presented—offline, online and hybrid SPM. To extract sequential patterns of preferably watched TV program contents, we propose a preference weighted normalized modified retrieval rank (PW-NMRR) metric for similar user clustering. In the offline SPM method, we effectively construct the sequential patterns for recommendation using a projection method, which yields good performance for relatively longer sequential patterns. The online SPM method mines sequential patterns online by effectively reflecting the recent preference characteristics of users for TV program contents, which is effective for short-sequence recommendation. The hybrid SPM method combines the offline and online SPM methods. The maximum precisions of 0.877, 0.793 and 0.619 for length-1, -2 and -3 sequence recommendations are obtained from the online, hybrid and offline SPM methods, respectively.

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Acknowledgments

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2012-01120197). This work was supported by the IT R&D program of MKE/KEIT. [10039161, Core UI technologies for improving Smart TV UX].

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Authors and Affiliations

  1. Department of Information and Communications Engineering, Korea Advanced Institute Science and Technology, Daejeon, Republic of Korea

    Shinjee Pyo & Munchurl Kim

  2. Department of Electrical Engineering, Korea Advanced Institute Science and Technology, Daejeon, Republic of Korea

    Eunhui Kim & Munchurl Kim

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  1. Shinjee Pyo
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  2. Eunhui Kim
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  3. Munchurl Kim
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Correspondence to Munchurl Kim.

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Pyo, S., Kim, E. & Kim, M. Automatic and personalized recommendation of TV program contents using sequential pattern mining for smart TV user interaction. Multimedia Systems 19, 527–542 (2013). https://doi.org/10.1007/s00530-013-0311-7

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