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

TL;DR

This paper introduces a novel dataset and model for recommending relevant tutorials for live-streamed videos, addressing the challenge of linking streaming content with educational tutorials to enhance learning.

Contribution

The work presents a new dataset and a discourse-level consistency and ontology-based filtering model specifically designed for tutorial recommendation in livestream videos.

Findings

The proposed model effectively captures discourse-level context.

Tutorial recommendation accuracy improves with discourse and ontology integration.

The dataset reveals the complexity of linking live streams with relevant tutorials.

Abstract

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.