  Textual data are commonly used as auxiliary information for modeling user
preference nowadays. While many prior works utilize user reviews for rating
prediction, few focus on top-N recommendation, and even few try to incorporate
item textual contents such as title and description. Though delivering
promising performance for rating prediction, we empirically find that many
review-based models cannot perform comparably well on top-N recommendation.
Also, user reviews are not available in some recommendation scenarios, while
item textual contents are more prevalent. On the other hand, recent graph
convolutional network (GCN) based models demonstrate state-of-the-art
performance for top-N recommendation. Thus, in this work, we aim to further
improve top-N recommendation by effectively modeling both item textual content
and high-order connectivity in user-item graph. We propose a new model named
Attentive Graph-based Text-aware Recommendation Model (AGTM). Extensive
experiments are provided to justify the rationality and effectiveness of our
model design.
