In this paper, we propose a novel large-scale, context-aware recommender system that provides accurate recommendations, scalability to a large number of diverse users and items, differential services, and does not suffer from “cold start” problems. Our proposed recommendation system relies on a novel algorithm which learns online the item preferences of users based on their click behavior, and constructs online item-cluster trees. The recommendations are then made by choosing an item-cluster level and then selecting an item within that cluster as a recommendation for the user. This approach is able to significantly improve the learning speed when the number of users and items is large, while still providing high recommendation accuracy. Each time a user arrives at the website, the system makes a recommendation based on the estimations of item payoffs by exploiting past context arrivals in a neighborhood of the current user's context. It exploits the similarity of contexts to learn how to make better recommendations even when the number and diversity of users and items is large. This also addresses the cold start problem by using the information gained from similar users and items to make recommendations for new users and items. We theoretically prove that the proposed algorithm for item recommendations converges to the optimal item recommendations in the long-run. We also bound the probability of making a suboptimal item recommendation for each user arriving to the system while the system is learning. Experimental results show that our approach outperforms the state-of-the-art algorithms by over 20 percent in terms of click through rates.
['Linqi Song', 'Cem Tekin', 'Mihaela van der Schaar']
Online Learning in Large-Scale Contextual Recommender Systems