Shopping recommender system for University students

Lim, Woon Sheng (2024) Shopping recommender system for University students. Final Year Project, UTAR.

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Abstract

This project is a development-based project that referred to e-commerce platform to buildan system that recommend a physical shop and store. This recommendation area focuses on sentiment analysis by using user’s review and rating to classify in to three categogy which are negative, neutral, and positive. This project idea came from the market us lacking a platform that focusing recommending a physical shop and store. While most of the platform is focusing on online shop and store, only certain number of a physical shop and store will have an online shop in the platform such as Lazada, and Shopee. Hence, this project is aimed to design a application that focuses on recommending physical shop and store by suing sentiment analysis on review and rating. The project used rapid applciation development methodology and agile development methodology for entire project development. Which provide fexlibility, agility, and effectiveness to the project’s process. For the system development itself, it is using methodology know as Cross-Industry Standard Process for Data Mining. It is start from business understanding until deployment phases with total 6 phases. The project developed an application that integrated with a sentient analysis model that used BERT model to train. It is able to generate the result for the review and rating. The application able to provide different criteria and requirement for user to select to perform filtering and ranking for shop and store recommendations. Lastly, the projectmain to contribute to the axisting platform and software that apply sentiment analysis in terms of the methods on utilization of the sentiment analysis result for filtering functions.

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