In the era of information explosion, recommender systems are widely studied and applied to explore users’ preferences. Reviews often reflect users’ real thoughts and play an important role in modeling user preferences. However, most existing review-based recommendation methods assign the same weight to words with different levels of importance, which degrades the recommendation performance. To address these problems, we propose a novel model that uses an adjusted cosine similarity to compute the preference matrix of user, then uses an incentive compression network to weight the preference matrix to focus on important local interaction features and achieve user rating prediction of items based on that interaction feature. The experimental results show that our model outperforms previous mainstream recommendation models.