Recommendation of who-to-follow and what-to-buy

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 bo...

Full description

Saved in:
Bibliographic Details
Main Author: Li, Chuqiao
Other Authors: Zhang Jie
Format: Final Year Project
Language:English
Published: 2017
Subjects:
Online Access:http://hdl.handle.net/10356/70652
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-70652
record_format dspace
spelling sg-ntu-dr.10356-706522023-03-03T20:26:20Z Recommendation of who-to-follow and what-to-buy Li, Chuqiao Zhang Jie School of Computer Science and Engineering DRNTU::Engineering::Computer science and engineering 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. Bachelor of Engineering (Computer Science) 2017-05-08T03:43:09Z 2017-05-08T03:43:09Z 2017 Final Year Project (FYP) http://hdl.handle.net/10356/70652 en Nanyang Technological University 42 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Computer science and engineering
spellingShingle DRNTU::Engineering::Computer science and engineering
Li, Chuqiao
Recommendation of who-to-follow and what-to-buy
description 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.
author2 Zhang Jie
author_facet Zhang Jie
Li, Chuqiao
format Final Year Project
author Li, Chuqiao
author_sort Li, Chuqiao
title Recommendation of who-to-follow and what-to-buy
title_short Recommendation of who-to-follow and what-to-buy
title_full Recommendation of who-to-follow and what-to-buy
title_fullStr Recommendation of who-to-follow and what-to-buy
title_full_unstemmed Recommendation of who-to-follow and what-to-buy
title_sort recommendation of who-to-follow and what-to-buy
publishDate 2017
url http://hdl.handle.net/10356/70652
_version_ 1759856074232430592