论文标题
使用话语级的一致性和基于本体的过滤的直播视频的教程建议
Tutorial Recommendation for Livestream Videos using Discourse-Level Consistency and Ontology-Based Filtering
论文作者
论文摘要
流视频是创作者与观众分享创意作品的方法之一。在这些视频中，流媒体通过在一个或几个用于创意项目的程序中使用各种工具来分享他们如何实现最终目标。为此，可以讨论实现最终目标所需的步骤。因此，这些视频可以提供大量的教育内容，这些内容可用于学习如何使用流媒体使用的工具。但是，缺点之一是流媒体可能无法为每个步骤提供足够的详细信息。因此，对于学习者来说，可能很难赶上所有步骤。为了减轻此问题，一种解决方案是将流视频与流视频中使用的工具可用的相关教程联系起来。更具体地说，系统可以分析实时流媒体视频的内容，并推荐最相关的教程。由于现有的文档推荐模型无法处理这种情况，因此在这项工作中，我们为实时传输视频的教程建议提供了一个新颖的数据集和模型。我们对拟议的数据集和模型进行了广泛的分析，揭示了该任务的挑战性质。
Streaming videos is one of the methods for creators to share their creative works with their audience. In these videos, the streamer share how they achieve their final objective by using various tools in one or several programs for creative projects. To this end, the steps required to achieve the final goal can be discussed. As such, these videos could provide substantial educational content that can be used to learn how to employ the tools used by the streamer. However, one of the drawbacks is that the streamer might not provide enough details for every step. Therefore, for the learners, it might be difficult to catch up with all the steps. In order to alleviate this issue, one solution is to link the streaming videos with the relevant tutorial available for the tools used in the streaming video. More specifically, a system can analyze the content of the live streaming video and recommend the most relevant tutorials. Since the existing document recommendation models cannot handle this situation, in this work, we present a novel dataset and model for the task of tutorial recommendation for live-streamed videos. We conduct extensive analyses on the proposed dataset and models, revealing the challenging nature of this task.
