  Recent deep learning methods for recommendation systems are highly
sophisticated. For article recommendation task, a neural network encoder which
generates a latent representation of the article content would prove useful.
However, using raw text with embedding for models could degrade sentence
meanings and deteriorate performance. In this paper, we propose PhrecSys
(Phrase-based Recommendation System), which injects phrase-level features into
content-based recommendation systems to enhance feature informativeness and
model interpretability. Experiments conducted on six months of real-world data
demonstrate that phrase features boost content-based models in predicting both
user click and view behavior. Furthermore, the attention mechanism illustrates
that phrase awareness benefits the learning of textual focus by putting the
model's attention on meaningful text spans, which leads to interpretable
article recommendation.
