Name
Title | ||
|---|---|---|
Deep Learning-Based Multi-Chatbot Broker for Q&A Improvement of Video Tutoring Assistant |
Abstract | ||
|---|---|---|
Chatbot is software for conversations where the opponent is a program instead of a human. Marketing, business, education, healthcare, and other fields are using chatbots for the convenience of users. The functionality of the chatbot is like a virtual assistant for users to help in purchasing and idea generations, resulting in better services and faster response. This paper presents the fundamental components and working principle of a multi-chatbot broker for video tutoring assistant for e-learning as an online video lecture assistant. The proposed broker implements a deep learning approach for a single video having multiple chatbots to select relative answers for a different perspective of each user, thereby improving the responsiveness of feedback and its quality. This means reducing the responding time of video tutoring assistant and increasing the quality of Q&A data by adding multiple chatbots from different viewpoints for a single video. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/BigComp48618.2020.00-71 | 2020 IEEE International Conference on Big Data and Smart Computing (BigComp) |
Keywords | DocType | ISSN |
chatbot,broker,deep learning,multi-chatbot,video tutoring | Conference | 2375-933X |
ISBN | Citations | PageRank |
978-1-7281-6035-1 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ozoda Makhkamova | 1 | 0 | 0.34 |
| Kang-Hee Lee | 2 | 0 | 0.34 |
| KyoungHwa Do | 3 | 0 | 0.34 |
| Doohyun Kim | 4 | 0 | 0.34 |