Spotify, as one of the most well-known digital music services, is widely used by music lovers worldwide. Spotify is a powerful platform as people can find songs they want in its music library. There is no doubt that Spotify is one of the most popular music platforms all around the world.
In recent years, chat bots are becoming a popular human-computer interaction application by which people can customize their needs in an intuitive way. An increasing number of services are adding chatbot modules to improve user experience and the development of chat bot is a trend of the era.
In this project, our team managed to create a chat bot which can respond to the user's input and perform interaction with the user's Spotify account. It can help them find songs they want, play a demo, and make recommendations. Users can talk with the chatbot, ask it some questions and ask for a recommendation for music in the Spotify library based on the artist’s name, music genre or artist genre. The chatbot is also capable of obtaining a user's playlist on Spotify and making recommendations based on it. A HTML frontend is created for the chatbot and backend is built based on the Flask framework. This chatbot only considers music from 2000 to 2020 included in the Spotify music library so it is still limited to some extent. Nevertheless, it still has potential to be further developed in functions and databases.
| Official Full Name | Student ID (MTech Applicable) | Work Items (Who Did What) | Email (Optional) |
|---|---|---|---|
| Chen Huizhou | A0261843A | Project ideation, Designing Dialogflow agent, Implementing Backend logic, Manipulating Spotify API, System debuging | [email protected] |
| Shi Qirui | A0261896M | Engaged data preprocessing, Designed recommendation model, Added authentication (sign up and login) using Flask, Integrated the whole code and debugging the system | [email protected] |
| Tang Junbo | A0226717B | Build the project framework, Implement frontend to backend data interaction, such as audio playing, Test connects to diologflow and backend through gcp, Test spotify certification. | [email protected] |
| Tian Hengyi | A0261841H | Generated the project idea, Designed the HTML frontend for chatbot using CSS, Engaged in data preprocessing, Generated the intents to be recognized by chatbot using Dialogflow, Debuged the chatbot | [email protected] |
Because we can't submit file that larger than 100MB to github, please find our video via the url below:
Since the user guide is a little bit long, we only put the rough steps here, please find all installation details in project report: Appendix4 User Guide at Github Folder: ProjectReport
Refer to project report at Github Folder: ProjectReport
Recommended Sections for Project Report / Paper:
- Executive Summary
- Business justification
- Knowledge modeling
- System design
- Conclusion
- Appendix 1: Project Proposal
- Appendix 2 Mapping Functionalities
- Appendix 3: Reference
- Appendix 4: User Guide
- Appendix 5: Individual Report–Shi Qirui
- Appendix 6: Individual Report–Tang Junbo
- Appendix 7: Individual Report–Tian Hengyi
- Appendix 8 Individual Report – Chen Huizhou
This Machine Reasoning (MR) course is part of the Analytics and Intelligent Systems and Graduate Certificate in Intelligent Reasoning Systems (IRS) series offered by NUS-ISS.
Lecturer: GU Zhan (Sam)
