论文标题
个性化电视推荐：融合用户行为和偏好
Personalized TV Recommendation: Fusing User Behavior and Preferences
论文作者
论文摘要
在本文中，我们提出了推荐线性电视节目的两阶段排名方法。提议的方法首先利用有关时间和电视频道的用户查看模式来确定潜在的候选人的建议，然后进一步利用用户偏好来对这些候选人进行排名，给出了有关程序的文本信息。为了评估该方法，我们对现实世界电视数据集进行了经验研究，其结果证明了我们模型的卓越性能，从建议的准确性和时间效率来看。
In this paper, we propose a two-stage ranking approach for recommending linear TV programs. The proposed approach first leverages user viewing patterns regarding time and TV channels to identify potential candidates for recommendation and then further leverages user preferences to rank these candidates given textual information about programs. To evaluate the method, we conduct empirical studies on a real-world TV dataset, the results of which demonstrate the superior performance of our model in terms of both recommendation accuracy and time efficiency.
