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STR-SA: Session-based Thread Recommendation for Online Course Forum with Self-Attention

机译：STR-SA：具有自我注意力的在线课程论坛的基于会话的线程推荐

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摘要

In recent years, along with the rapid development of Massive Open Online Courses (MOOCs), a large number of students participate in MOOC courses. MOOC course forum is very important for students discussing questions related to online courses when they don’t have enough offline resources to get help from. There are a lot of qualified discussion threads in MOOC courses which are helpful for understanding the course materials. However, students are always overwhelmed by the enormous amount of threads. A solution to alleviate this problem is to recommend a list of threads for each student that she/he may be interested in. Existing thread recommender systems are insufficient to capture the complex relationships among the threads in a student’s visit session, and do not simultaneously take into account the student’s global preference, as well as her/his current interest in that session. In this work, we propose a novel neural network framework for session-based thread recommendation (STR-SA for short). The proposed method, which recommends threads to a student based on the threads viewed by the student in the current session, learns the relationships among threads by applying self-attention mechanism. Furthermore, we capture the global preference of the student by combining the viewed threads, and consider the latest-viewed thread as the current interest of the student. Extensive experiments conducted on three course-forum datasets show that the proposed model STR-SA significantly outperforms other representative methods for MOOC thread recommendation.
机译：近年来，随着大规模在线公开课程（MOOC）的快速发展，大量学生参加了MOOC课程。如果学生没有足够的离线资源来寻求帮助，则MOOC课程论坛对于学生讨论与在线课程相关的问题非常重要。 MOOC课程中有很多合格的讨论线程，有助于理解课程资料。但是，大量的线程总是让学生不知所措。缓解此问题的一种方法是为她/他可能感兴趣的每个学生推荐一个线程列表。现有的线程推荐器系统不足以捕获学生的访问会话中线程之间的复杂关系，并且不能同时采取考虑到学生的整体偏好，以及他/她目前在该课程中的兴趣。在这项工作中，我们为基于会话的线程推荐（简称STR-SA）提出了一种新颖的神经网络框架。所提出的方法基于学生在当前会话中查看的线程向学生推荐线程，该方法通过应用自我注意机制来学习线程之间的关系。此外，我们通过结合查看的线程来捕获学生的全局偏好，并将最新查看的线程视为学生当前的兴趣。在三个课程论坛数据集上进行的大量实验表明，所提出的STR-SA模型明显优于其他具有代表性的MOOC线程推荐方法。

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