Online learning-based clustering approach for news recommendation systems

Recommender agents are widely used in online markets, social networks and search engines. The recent online news recommendation systems such as Google News and Yahoo! News produce real-time decisions for ranking and displaying highlighted stories from massive news and users access per day. The more relevant highlighted items are suggested to users, the more interesting and better feedback from users achieve. Therefore, the distributed online learning can be a promising approach that provides learning ability for recommender agents based on side information under dynamic environment in large scale scenarios. In this work, we propose a distributed algorithm that is integrated online K-Means user contexts clustering with online learning mechanisms for selecting a highlighted news. Our proposed algorithm for online clustering with lower bound confident clustering approximates closer to offline K-Means clusters than greedy clustering and gives better performance in learning process. The algorithm provides a scalability, cheap storage and computation cost approach for large scale news recommendation systems.