A Regularized Model to Trade off between Accuracy and Diversity in a News Recommender System
News recommender systems are usually designed to provide accurate and personalized recommendations to the readers. The diversity of the recommended results has received much less attention in this field. When it is considered, the current state of the art models often apply the re ranking mechanisms to promote the diversified results to the individual users. In this work, we propose a latent factor model to achieve the requisite level of accuracy while maintaining a reasonable level of diversity in a news recommender system. The existing latent factor methods mostly rely on Tikhonov regularization to improve the generality of the learnt models. These methods tend to focus mainly on accuracy measures, i.e. generating recommendations highly aligned with a user's past preference, which may cause a decrease in the diversity of information to which news readers are exposed. In our work, we make effective use of elastic net regression to regularize the model for both the accuracy and the diversity in a single optimization framework. We demonstrate the effectiveness of our model over the state of the art methods by conducting extensive experiments on a real world news dataset.
2020 IEEE International Conference on Big Data (Big Data)