News Recommendation Based on Collaborative Semantic Topic Models and Recommendation Adjustment

Abstract

Providing news recommendations is an important trend for online news websites to attract more users and create more benefits. In this research, we propose a novel recommendation approach for recommending news articles. We propose A Collaborative Semantic Topic Model and an ensemble model to predict user preferences based on combining Matrix Factorization with articles' semantic latent topics derived from word embedding and Latent Dirichlet Allocation. The proposed ensemble model is further integrated with a recommendation adjustment mechanism to adjust users' online recommendation lists. We evaluate the proposed approach via offline experiments and online evaluation on a real news website. The experimental result demonstrates that our proposed approach can improve the recommendation quality of recommending news articles.