Contextual Bandit Approach-based Recommendation System for Personalized Web-based Services

In recent years, recommendation systems have started to gain significant attention and popularity. A recommendation system plays a significant role in various applications and services such as e-commerce, video streaming websites, etc. A critical task for a recommendation system is to model users’ preferences so that it can attain the capability to suggest personalized items for each user. The personalized list suggested by a suitable recommendation system should contain items highly relevant to the user. However, many a times, the traditional recommendation systems do not have enough data about the user or its peers because the model faces the cold-start problem. This work compares the existing three MAB algorithms: LinUCB, Hybrid-LinUCB, and CoLin based on evaluating regret. These algorithms are first tested on the synthetic data and then used on the real-world datasets from different areas: Yahoo Front Page Today Module, Lastfm, and MovieLens20M. The experiment results show that CoLin outperforms Hybrid-LinUBC and LinUCB, reporting cumulated regret of 8.950 for LastFm and 60.34 for MovieLens20M and 34.10 for Yahoo FrontPage Today Module.