  One of the main challenges in recommender systems is data sparsity which
leads to high variance. Several attempts have been made to improve the
bias-variance trade-off using auxiliary information. In particular, document
modeling-based methods have improved the model's accuracy by using textual data
such as reviews, abstracts, and storylines when the user-to-item rating matrix
is sparse. However, such models are insufficient to learn optimal
representation for users and items. User-based and item-based collaborative
filtering, owing to their efficiency and interpretability, have been long used
for building recommender systems. They create a profile for each user and item
respectively as their historically interacted items and the users who
interacted with the target item.
  This work combines these two approaches with document context-aware
recommender systems by considering users' opinions on these items. Another
advantage of our model is that it supports online personalization. If a user
has new interactions, it needs to refresh the user and item history
representation vectors instead of updating model parameters. The proposed
algorithm is implemented and tested on three real-world datasets that
demonstrate our model's effectiveness over the baseline methods.
