# Personalized Music Recommendation with Triplet Network

## TL;DR

This paper introduces a triplet neural network approach for personalized music recommendation, leveraging positive and negative samples to improve user-item feature representations and address common recommendation challenges.

## Contribution

The paper presents a novel triplet neural network model specifically designed for music recommendation, enhancing representation learning and distance measurement between users and items.

## Key findings

- Improved recommendation accuracy over baseline methods
- Effective handling of cold start problems
- Enhanced feature representation for users and music items

## Abstract

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.