Semantic Analysis and Preference Capturing on Attentive Networks for Rating Prediction

Nowadays, people receive an enormous amount of information from day to day. However, they are only interested in information which matches their preferences. Thus, retrieving such information becomes an significant task, in our case, the reviews composed by users. Matrix Factorization (MF) based methods achieve fairly good performances on recommendation tasks. However, there exist several crucial issues with MF - based methods such as cold-start problems and data sparseness. In order to address the above issues, numerous recommendation models are proposed which obtained stellar performances. Nonetheless, we figured that there is not a more comprehensive framework that enhances its performance through retrieving user preference and item trend. Hence, we propose a novel approach to tackle the aforementioned issues. A hierarchical construction with user preference and item trend capturing is employed in this proposed framework. The performance excels in comparison to state-of-the-art models by testing on several real-world datasets. Experimental results verified that our framework can extract useful features even under sparse data.