  Influenced by the great 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 from the perspective of recommendation modeling
with the accuracy goal, aiming to summarize this field to facilitate
researchers and practitioners working on recommender systems. Specifically,
based on the data usage during recommendation modeling, we divide the work into
collaborative filtering and information-rich recommendation: 1) collaborative
filtering, which leverages the key source of user-item interaction data; 2)
content enriched recommendation, which additionally utilizes the side
information associated with users and items, like user profile and item
knowledge graph; and 3) temporal/sequential recommendation, which accounts for
the contextual information associated with an interaction, such as time,
location, and the past interactions. After reviewing representative work for
each type, we finally discuss some promising directions in this field.
