线性电视推荐中节目类型的时间相似性可视化

Veronika Bogina, Julia Sheidin, T. Kuflik, S. Berkovsky
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引用次数: 2

摘要

越来越多的证据表明,数据可视化是快速理解和过滤大量数据的重要而有用的工具。在本文中,我们通过一项比较和弦和排名列表在下一个节目推荐中呈现时间电视节目类型相似性的研究来促进这一工作。我们基于时间观看模式的相似性来考虑类型相似性。我们发现,和弦表示可以让用户看到整个画面,并提高他们选择项目的能力,而不是排名在前的类似项目列表。我们相信,相似性可视化对于向最终用户提供推荐及其解释可能是有用的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Visualizing Program Genres' Temporal-Based Similarity in Linear TV Recommendations
There is an increasing evidence that data visualization is an important and useful tool for quick understanding and filtering of large amounts of data. In this paper, we contribute to this body of work with a study that compares chord and ranked list for presentation of a temporal TV program genre similarity in next-program recommendations. We consider genre similarity based on the similarity of temporal viewing patterns. We discover that chord presentation allows users to see the whole picture and improves their ability to choose items beyond the ranked list of top similar items. We believe that similarity visualization may be useful for the provision of both the recommendations and their explanations to the end users.
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