(2014) proposed a Top-icMF method to represent users and items by incorporate the topics extracted from review texts to enhance the learning of latent user and item factors from the rating matrix via non-negative matrix factorization (NMF).
these methods only extract topics from reviews, and cannot effectively utilize the contexts and word orders in reviews, both of which are important for learning accurate user and item representations.