Movie Recommendations
Undergraduate research thesis
About This Work #
This dissertation explores Using User Inputs and Individual Preferences to Enhance the Movie Recommendation, with a Netflix case study. The research was conducted at The University of Manchester and completed in 2023.
Abstract #
Movies have long been one of the most common and beloved forms of entertainment. However, in the age of digitization, users often struggle with the “paradox of choice” on streaming platforms. Even with personalized recommendations, many spend considerable amounts of time in the search for a movie that meets their desires. This research employs a design science methodology approach to understand how user inputs can enhance the user experience on streaming platforms and the recommendations provided, with a focus on the Netflix website. The research explores what factors influence movie recommendation accuracy, as well as the most important criteria people have when choosing a movie, using an online questionnaire. The study develops a website that is a clone of Netflix which has an additional filtering functionality to reflect the identified criteria. This was tested by 10 participants who were also interviewed regarding their experience with it. The results show that the website was perceived positively, with most people acknowledging that such a solution would reduce the time they spend browsing, by reducing the number of recommendations they receive, and enhancing these by limiting to only what they want to see based on personal preferences or mood.