Facing Cold-Start: A Live TV Recommender System Based on Neural Networks
Facing Cold-Start: A Live TV Recommender System Based on Neural Networks
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DOI：
10.1109/access.2020.3007675
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发表时间：
2020
期刊：
影响因子：
3.9
通讯作者：
Xiaosong Zhu;Jingfeng Guo;Shuang Li;Tong Hao
中科院分区：
文献类型：
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作者：
Xiaosong Zhu;Jingfeng Guo;Shuang Li;Tong Hao
随着电视直播频道数量的增加，观众必须花费越来越多的时间和精力来决定观看哪些节目，这个问题被称为信息过载，推荐系统（RS）是解决这些问题的有效方法。由于电视节目的高更新率和低重播率，节目冷启动问题突出，严重影响了推荐系统的有效性，限制了推荐算法在电视直播中的应用。为了更好地解决这个问题，RS必须在依赖经验的时隙策略之外考虑信息。目前，还没有一种方法能够很好地利用观看行为记录。因此，在本文中，我们提出了一种称为DeepTV的观看环境模型，该模型考虑了观看行为记录和电子节目指南，并包括特征生成过程和模型构建过程。在特征生成过程中，通过收视时间聚类、正负反馈区分、连续收视偏好捕捉和候选节目剩余时间比例的引入定义了7个关键特征。我们对连续特征进行归一化处理，并对连续特征进行幂次叠加。在模型构建过程中，我们将电视直播推荐任务视为一个分类问题，并使用神经网络融合上述特征。最后，工业数据集上的实验表明，该模型显着优于基线算法。
With the increase in the number of live TV channels, audiences must spend increasing amounts of time and energy deciding which shows to watch; this problem is called information overload, and recommender systems (RSs) are effective methods for addressing such problems. Due to the high update rates and low replay rates of TV programs, the item cold-start problem is prominent, and this problem seriously affects the effectiveness of the recommender and limits the application of recommendation algorithms for live TV. To solve this problem better, RSs must consider information in addition to the time slot strategy, which relies on experience. At present, no methods make good use of viewing behavior records. Therefore, in this paper, we proposed a viewing environment model called DeepTV that considers viewing behavior records and electronic program guides and includes a feature generation process and a model construction process. In the feature generation process, we defined seven key features by clustering viewing time, distinguishing positive and negative feedback, capturing continuous viewing preference and introducing the remaining time proportion of candidate programs. We normalize the continuous features and add powers of them. In the model construction process, we regard the live TV recommendation task as a classification problem and fuse the above features by using a neural network. Finally, experiments on industrial datasets show that the proposed model significantly outperforms baseline algorithms.