Companies have been changing and developing their marketing tools and techniques in order to follow the rapid growth of e-commerce in many aspects, and they specifically try to target customers by offering them the products and services they need using recommender engines. Moreover, the rapid growth in e-commerce resulted in people placing the web as the source of information to buy or sell from. Therefore, other than normal stores, many online shops exist, in different forms, from websites with private domain to thread in online forums. This has advantages which is that people have more options to shop from, but at the same time it is also a disadvantage where with so many options, customers will find difficulties to choose which store is more suitable to buy a product from. In this capstone project we study the time consumed by the customer to find a suitable website to buy a desired device using a recommender data analytics approach. The purpose of the project is to build a recommender system that recommends a store to buy a product from based on the user entry parameters. As well as to help the stores to increase their ranking in the recommender engine by using analytical models. Data will be extracted from electronic stores in the UAE. Data was visualized, preprocessed, and suitable attributes were chosen before building the models.
Professional Studies (MS)
Department, Program, or Center
Graduate Programs & Research (Dubai)
Alkhzami, Fatmah Ahmed Ali, "Retail Store Ranking Recommender System" (2020). Thesis. Rochester Institute of Technology. Accessed from