Abstract
Proper marketing requires a great understanding of customers' needs and how they can be presented to them properly so that they purchase the services provided by companies and generate high profits. Recommender systems are considered as an answer to this marketing scheme; through feeding it enough data on customers' purchase history, it can understand what customers usually procure and what items can be proposed to them so that more sales can be conducted, resulting in higher profits. However, with the increasing number of techniques and algorithms for recommender systems, it reduced the rate of its accuracy toward presenting customers with the right items. In this study, Amazon's marketing bias and its recommender system will be researched and apply the many data analytics and mining techniques to further enhance their accuracy and the rate of a customer purchasing another item based on the recommender system. Moreover, the study will determine which recommender system paradigms is most suited for Amazon.com, either Collaborative or Content-based methods.
Publication Date
12-2020
Document Type
Master's Project
Student Type
Graduate
Degree Name
Professional Studies (MS)
Department, Program, or Center
Graduate Programs & Research (Dubai)
Advisor
Sanjay Modak
Advisor/Committee Member
Ehsan Warriach
Recommended Citation
Alolama, Yousuf, "Recommender Systems and Amazon Marketing Bias" (2020). Thesis. Rochester Institute of Technology. Accessed from
https://repository.rit.edu/theses/10726
Campus
RIT Dubai
