A software developer with over 8 years experience in Web application development and Business Intelligence applications.
Project Summary
Project Title: A Recommender System for a Local Video Streaming Service
Research Supervisor: Dr. Lawrence Muchemi
Abstract: In the past few years, we have witnessed explosive growth in e-commerce and online streaming services. This has led to ever-growing upload of new content every day, which presents an information overload challenge to users. To filter information from these huge volumes of data and present products that are of interest to users is a very difficult task. To solve this problem, organizations are deploying recommender system to provide suggestions to products that might be of interest to the user. Recommender systems have become prominent in modern web applications such as streaming services and e-commerce due to their capability to personalize user experience by providing items suggestion that the customer will most likely buy. Traditionally this systems have been based on neighborhood techniques and latent factor models. However, these models have not been efficient in utilizing sequential features of historical user transactions. This research designs a deep learning model based on recurrent neural network that processes sequential user-item interaction with data in order to provide personalized recommendations to users based on their past interactions. The outcome of our study show that recurrent neural networks can also achieve good predictive results in recommender systems domain.