透過社群媒體評論及選擇性使用者偏好建立 Embedding-based 音樂推薦系統

Abstract

With online music streaming and subscription becoming the mainstream way of listening to music, people have more songs to choose from. Therefore, the importance of the music recommendation system is more important than before. It can not only reduce the time for users to find songs that they are interested in but explore the potential interests of users. In our research, we build a personalization embedding-based recommendation system for J-pop music, and we will focus on how to capture the user preference more precisely. We use the side information of the song to represent the reason that people may be interested in the song. We consider the relation between these user preferences by domain knowledge. Moreover, we collect reviews on social media as one of the user preferences. We found that people tend to write reviews on social media platforms to describe the feelings of the song, and it brings information that is different from the traditional user preference. When recommending, we consider the long-term and short-term user preferences. The long-term user preference indicates the user's music habit, and the event or the purpose of users listening to music will impact the short-term user preference. Besides, we assume that people listen to different types of music for different reasons and preferences. It means that the user preference will vary with the song, and we called it selective user preference. We design an embedding-based recommendation system by deep learning model. The model includes a knowledge graph and the attention mechanism. Then, we hold an ablation experiment to evaluate the performance of the concept we implement. After successful on the ablation experiment, we discuss the result and provide some findings to prove the concept is reasonable.