  Recommendation has been a long-standing problem in many areas ranging from
e-commerce to social websites. Most current studies focus only on traditional
approaches such as content-based or collaborative filtering while there are
relatively fewer studies in hybrid recommender systems. Due to the latest
advances of deep learning achieved in different fields including computer
vision and natural language processing, deep learning has also gained much
attention in Recommendation Systems. There are several studies that utilize ID
embeddings of users and items to implement collaborative filtering with deep
neural networks. However, such studies do not take advantage of other
categorical or continuous features of inputs. In this paper, we propose a new
deep neural network architecture which consists of not only ID embeddings but
also auxiliary information such as features of job postings and candidates for
job recommendation system which is a reciprocal recommendation system.
Experimental results on the dataset from a job-site show that the proposed
method improves recommendation results over deep learning models utilizing ID
embeddings.
