Abstract The performance of a recommendation system relies heavily on the feedback of users. Most of the traditional recommendation algorithms based only on historical ratings will encounter several difficulties given the problem of data sparsity. Users’ feedback usually contains rich textual reviews in addition to numerical ratings. In this paper, we exploit textual review information, as well as ratings, to model user preferences and item features in a shared topic space and subsequently introduce them into a matrix factorization model for recommendation. To this end, the data sparsity problem is alleviated and good interpretability of the recommendation results is gained. Another contribution of this work is that we model the item feature distributions with rating-boosted reviews which combine textual reviews with user sentiments. Experimental results on 26 real-world datasets from Amazon demonstrate that our approach significantly improves the rating prediction accuracy compared with various state-of-the-art models, such as LFM, HFT, CTR and RMR models. And much higher improvement is achieved for users who have few ratings, which verifies the effectiveness of the proposed approach for sparse data. Moreover, our method also benefits much from reviews on top-N recommendation tasks.