Up Next: Retrieval Methods for Large Scale Related Video Suggestion

被引:21
作者
Bendersky, Michael [1 ]
Garcia-Pueyo, Lluis [1 ]
Harmsen, Jeremiah [1 ]
Josifovski, Vanja [1 ]
Lepikhin, Dima [1 ]
机构
[1] Google Inc, Santa Clara, CA 95050 USA
来源
PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14) | 2014年
关键词
Video retrieval; related video suggestion; video representation;
D O I
10.1145/2623330.2623344
中图分类号
TP18 [人工智能理论];
学科分类号
140502 [人工智能];
摘要
The explosive growth in sharing and consumption of the video content on the web creates a unique opportunity for scientific advances in video retrieval, recommendation and discovery. In this paper, we focus on the task of video suggestion, commonly found in many online applications. The current state-of-the-art video suggestion techniques are based on the collaborative filtering analysis, and suggest videos that are likely to be co-viewed with the watched video. In this paper, we propose augmenting the collaborative filtering analysis with the topical representation of the video content to suggest related videos. We propose two novel methods for topical video representation. The first method uses information retrieval heuristics such as tf-idf, while the second method learns the optimal topical representations based on the implicit user feedback available in the online scenario. We conduct a large scale live experiment on YouTube traffic, and demonstrate that augmenting collaborative filtering with topical representations significantly improves the quality of the related video suggestions in a live setting, especially for categories with fresh and topically-rich video content such as news videos. In addition, we show that employing user feedback for learning the optimal topical video representations can increase the user engagement by more than 80% over the standard information retrieval representation, when compared to the collaborative filtering baseline.
引用
收藏
页码:1769 / 1778
页数:10
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