Leveraging Moderate User Data for News Recommendation

It is very crucial for news aggregator websites which are recent in the market to actively engage its existing users. A recommendation system would help to tackle such a problem. However, due to the lack of sufficient amount of data, most of the state-of-the-art methods perform poorly in terms of recommending relevant news items to the users. In this paper, we propose a novel approach for Item-based Collaborative filtering for recommending news items using Markov Decision Process (MDP). Due to the sequential nature of news reading, we choose MDP to model our recommendation system as it is based on a sequence optimization paradigm. Further, we also incorporate factors like article freshness and similarity into our system by extrinsically modelling it in terms of reward for the MDP. We compare it with various other state-of-the-art methods. On a moderately low amount of data we see that our MDP-based approach outperforms the other approaches. One of the reasons for this is that the baselines fail to identify the underlying patterns within the sequence in which the articles are read by the users. Hence, the baselines are not able to generalize well.