Experience Individualization on Online TV Platforms through Persona-based Account Decomposition

Payal Bajaj, Sumit Shekhar
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引用次数: 13

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.
通过基于人物的账户分解体验在线电视平台的个性化
近年来,网络电视发展迅速,大多数大型媒体公司都在网上播放他们的线性内容。访问在线电视帐户受到身份验证的保护,并且与传统有线电视订阅一样,同一家庭中的用户共享在线电视凭据。然而,由于标准的数据收集技术只能收集用户级别的信息,因此在线电视测量无法捕捉共享用户中个人级别的观看特征。因此,个人形象识别和体验个性化是网络电视平台面临的挑战和难点。在本文中,我们提出了一种新的方法,通过分析观看特征,将网络电视帐户分解为不同的角色共享帐户。然后提出了一种推荐算法来个性化每个角色的体验。最后,我们通过在大型在线电视数据库上的实验证明了所提出方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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