Ranking-Based Recommendation System with Text Modeling

In the study of recommendation systems, many methods based on predicting ratings have been put forward. However, the rating-predicting methods have some shortages. It pays too much attention to predicting, instead of the nature of recommendation, which is predicting the order of ratings. Thus, we use a pairwise-based learning algorithm to learn our model and take the zero-sampling method to improve our model. In addition, we propose a text modeling method making the recommendations more explicable. It is proved that our system performs better than other state-of-art methods.