Abstract
Online TV has seen rapid growth in recent years, with most of the large media companies broadcasting their linear content online. Access to the online TV accounts is protected by an authentication, and like the traditional cable TV subscription, users in the same household share the online TV credentials. However, as the standard data collection techniques have capability to collect only account level information, online TV measurements fail to capture individual level viewing characteristics in shared accounts. Thus, individual profile identification and experience individualization are challenging and difficult for online TV platforms. In this paper, we propose a novel approach to decompose online TV account into distinct personas sharing the account through analyzing viewing characteristics. A recommendation algorithm is then proposed to individualize the experience for each persona. Finally, we demonstrate the usefulness of the proposed approach through experiments on a large online TV database.
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