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|Title:||Recommendation of who-to-follow and what-to-buy||Authors:||Li, Chuqiao||Keywords:||DRNTU::Engineering::Computer science and engineering||Issue Date:||2017||Abstract:||This project intends to provide a one-stop solution for businesses who want to integrate to an online shopping platform to engage their customers. The project features s recommendation system that predicts customers’ ratings of products. In order to achieve this, a database is designed to capture both explicit and implicit feedbacks relating to customers’ browsing, searching, purchasing and rating histories. On top of the recommendation system, since mobile devices has become the major Internet traffic driver, the project is meant to support the database accessibility from both web applications and mobile applications. The project is completed under collaborative efforts from another Final Year Project participant, who is in charge of mobile application development. The first part of the report describes the motivations, plans and background of the project. The second part of the report explains the problems encountered as well as the methods to resolve them. The third part of the report elaborates the database design and APIs to access the database from mobile applications. The fourth part of the report demonstrates the user interface of the shopping website. The last part concludes the project and points out possible future development. The main consideration of the project is to develop a website application with all necessary functionalities for real-world business. Thus, Django Web Framework is selected to implement the website because it provides an elegant solution to build web applications on time by providing many open source libraries. The most significant problem encountered is how to ensure database synchronization across different platforms. After several attempts, we decided to use Django Rest Framework to provide APIs to mobile applications. We sincerely hope this project could integrate two hot topics in computer science effectively – the analysis based on Big Data and Machine Learning, as well as mobile application development. In this way, the project creates opportunities for businesses to provide better service to their customers and differentiate themselves to success in the competitive environment.||URI:||http://hdl.handle.net/10356/70652||Rights:||Nanyang Technological University||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Student Reports (FYP/IA/PA/PI)|
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