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Streaming videos is one of the methods for creators to share their creativeworks with their audience. In these videos, the streamer share how they achievetheir final objective by using various tools in one or several programs forcreative projects. To this end, the steps required to achieve the final goalcan be discussed. As such, these videos could provide substantial educationalcontent 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 enoughdetails for every step. Therefore, for the learners, it might be difficult tocatch up with all the steps. In order to alleviate this issue, one solution isto link the streaming videos with the relevant tutorial available for the toolsused in the streaming video. More specifically, a system can analyze thecontent 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 tutorialrecommendation for live-streamed videos. We conduct extensive analyses on theproposed dataset and models, revealing the challenging nature of this task.

Tutorial Recommendation for Livestream Videos using Discourse-Level Consistency and Ontology-Based Filtering