Abstract
While interactive television enables a new user-centered TV mode, catering to the tastes of TV users is one of the most critical tasks in delivering interactive TV experience. It faces two key challenges. First, the user behaviors on TV are much sparser than those of the internet users, thus making the modeling of user preferences more challenging. Second, an TV account is usually associated with multiple individuals in a family, making it difficult to discriminate the preferences of individual family members. In this paper, we thus propose a novel Clustering-Coupled Topic Model (CCTM), which characterizes user profile only by analyzing user viewing behaviors without any program metadata. This model clusters the users into different groups, then access the group preference for program recommendation by coupling the interest of different users in the same cluster group. We validate the performance of the CCTM with real-world data from a national interactive TV program. The experimental results have demonstrated that the CCTM can reasonably extract the users’ potential preference, which is further leveraged to recommend programs to the users.
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Jiang, Z. et al. (2018). Cold-Start Group Profiling with a Clustering-Coupled Topic Model. In: Zhai, G., Zhou, J., Yang, X. (eds) Digital TV and Wireless Multimedia Communication. IFTC 2017. Communications in Computer and Information Science, vol 815. Springer, Singapore. https://doi.org/10.1007/978-981-10-8108-8_31
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DOI: https://doi.org/10.1007/978-981-10-8108-8_31
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