Mining Latent Features from Reviews and Ratings for Item Recommendation
Chenyu Zhai, Jing Peng · 2016
In this paper, we propose a probabilistic model based on collaborative filtering and extended topic model for item recommendation. It allows us to extract the item features or user preferences which are represented with meaningful phrases. We develop efficient inference algorithms using Gibbs-EM sampling for posterior inference of our model. We evaluate the model on Amazon review dataset and the experiment results show that our model outperforms state-of-the-art methods on the task of recommendation.