  Since many online music services emerged in recent years so that effective
music recommendation systems are desirable. Some common problems in
recommendation system like feature representations, distance measure and cold
start problems are also challenges for music recommendation. In this paper, I
proposed a triplet neural network, exploiting both positive and negative
samples to learn the representation and distance measure between users and
items, to solve the recommendation task.
