面向视频冷启动问题的点击率预估
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董建锋,E-mail:dongjf24@gmail.com

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国家自然科学基金(61902347);浙江省自然科学基金(LQ19F020002,LGF21F020010)


Click-through Rate Prediction for Video Cold-start Problem
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    摘要:

    视频的点击率预估是视频推荐系统中的重要任务之一,推荐系统可以根据点击率的预估调整视频推荐顺序以提升视频推荐的效果.近年来,随着视频数量的爆炸式增长,视频推荐的冷启动问题也变得愈发严重.针对这个问题,本文提出了一个新的视频点击率预估模型,通过使用视频的内容特征以及上下文特征来加强视频点击率预估的效果;同时通过对冷启动场景的模拟训练和基于近邻的替代方法提升模型应对新视频点击率预估的能力.本文提出的模型可以同时对旧视频和新视频进行点击率预估.在两个真实的电视剧(Track_1_series)和电影(Track_2_movies)点击率预估数据集上的实验表明,本文提出的模型可以显著改善对旧视频的点击率预估性能,并在两个数据集上均超过了现有的模型;对于新视频,相比于不考虑冷启动问题的模型只能获得0.57左右的AUC性能,本文模型在两个数据集上分别获得0.645和0.615的性能,表现出针对冷启动问题更好的鲁棒性.

    Abstract:

    Video Click-Through Rate (CTR) Prediction is one of the important tasks in the context of video recommendation. According to click-through prediction, recommendation systems can adjust the order of the recommended video sequence to improve the performance of video recommendation. In recent years, with the explosive growth of videos, the problem of video cold start has become more and more serious. Aim for this problem, we propose a novel video click-through prediction model which utilizes both the video content features and context features to improve CTR prediction; we also propose a simulation training of the cold start scenario and neighbor-based new video replacement method to enhance the model's CTR prediction ability for new videos. Our proposed model is able to predict CTR for both old and new videos. The experiments on two real-world video CTR datasets (Track_1_series and Track_2_movies) show the effectiveness of our proposed method. Specifically, our proposed model using both video content and contextual information improves the performance of CTR prediction for old videos, which also outperforms the existing models on both datasets. Additionally, for new videos, a baseline model without considering the cold start problem achieves an AUC score of about 0.57. By contrast, our proposed model gives much better AUC scores of 0.645 and 0.615 on Track_1_series and Track_2_movies, respectively, showing the better robustness to the cold start problem.

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章磊敏,董建锋,包翠竹,纪守领,王勋.面向视频冷启动问题的点击率预估.软件学报,,():0

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  • 收稿日期:2021-01-09
  • 最后修改日期:2021-04-11
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  • 在线发布日期: 2021-11-24
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