  Influenced by the stunning success of deep learning in computer vision and
language understanding, research in recommendation has shifted to inventing new
recommender models based on neural networks. In recent years, we have witnessed
significant progress in developing neural recommender models, which generalize
and surpass traditional recommender models owing to the strong representation
power of neural networks. In this survey paper, we conduct a systematic review
on neural recommender models, aiming to summarize the field to facilitate
future progress. Distinct from existing surveys that categorize existing
methods based on the taxonomy of deep learning techniques, we instead summarize
the field from the perspective of recommendation modeling, which could be more
instructive to researchers and practitioners working on recommender systems.
Specifically, we divide the work into three types based on the data they used
for recommendation modeling: 1) collaborative filtering models, which leverage
the key source of user-item interaction data; 2) content enriched models, which
additionally utilize the side information associated with users and items, like
user profile and item knowledge graph; and 3) context enriched models, which
account for the contextual information associated with an interaction, such as
time, location, and the past interactions. After reviewing representative works
for each type, we finally discuss some promising directions in this field,
including benchmarking recommender systems, graph reasoning based
recommendation models, and explainable and fair recommendations for social
good.
