In real-world recommendation tasks, feedback data are usually sparse. Therefore, a
recommender's performance is often determined by how much information that it can extract
from textual contents. However, current methods do not make full use of the semantic
information. They encode the textual contents either by “bag-of-words” technique or
Recurrent Neural Network (RNN). The former neglects the order of words while the latter
ignores the fact that textual contents can contain multiple topics. Besides, there exists a …