Design and development of a video-streaming recommendation system

Abstract

Recommendation systems are being widely adopted for two main reasons: address the limitations of search engines in the era of Big Data, and improve the user experience by helping the user find what they want but cannot express because it is hard to state as a query or because they do not even know it exists. In the case of video-on-demand services, this can be critical to keep users engaged and avoid churn.

The purpose of this project has been to develop and evaluate different recommendation algorithms to find what combination of factors can be used to simulate user behavior more accurately in a video-on-demand setting. Using the recommendation of the most popular items as a baseline, five different algorithms (with multiple parameter variations) have been tested. The results have shown that combining multiple simple models provides the best results, and that taking into consideration the context in which the recommendation is made can heavily improve the performance.