Search based Video Recommendations

2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) Pub Date : 2013-12-01 DOI:10.1109/NCVPRIPG.2013.6776190

Abhranil Chatterjee, Bijoy Sarkar, Prateeksha Chandraghatgi, K. Seal, Girish Ananthakrishnan

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引用次数: 2

Abstract

In this paper, we present a search powered approach we have used in building a Video Recommendations Engine for Yahoo hosted videos and Yahoo Video Search. The aim is to increase user engagement by recommending related videos and hence increase revenue by being able to show more advertisements as the user keeps consuming more videos. This system accepts an input context which provides information about the user and the video consumed and returns a set of related videos as recommended. We look at this problem as a multi-faceted problem since the intent of the user at a particular point in time cannot be known deterministically. So we generate the candidate set of recommendations using an ensemble of algorithms and available search signals. We discuss these algorithms and mechanisms for retrieving related videos in details along with an explore-exploit strategy to learn a near optimal ranking of the candidate recommendations, and provide the performance results. This system has been able to increase the number of video plays at Yahoo by 66%.

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基于搜索的视频推荐

在本文中，我们提出了一种搜索驱动的方法，我们已经使用它为雅虎托管的视频和雅虎视频搜索构建了一个视频推荐引擎。其目的是通过推荐相关视频来提高用户参与度，从而随着用户不断消费更多视频，通过展示更多广告来增加收入。该系统接受一个输入上下文，该上下文提供有关用户和所消费视频的信息，并返回一组推荐的相关视频。我们把这个问题看作是一个多方面的问题，因为用户在特定时间点的意图是无法确定的。因此，我们使用算法集合和可用的搜索信号生成候选推荐集。我们详细讨论了这些检索相关视频的算法和机制，以及一种探索利用策略，以学习候选推荐的接近最优排名，并提供性能结果。该系统使雅虎的视频播放量增加了66%。

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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来源期刊

2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)

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