关键词: 音乐推荐, 因子分解机, 行为预测, 数据挖掘

Abstract: Traditional music rating recommendation model has lower accuracy, and recommended accuracy has received a great impact, because of user insufficient score and large subjective difference. Mining user interest from huge history behavior data is an excellent method to address those problems of rating model. Features are extracted from user behavior data to establish feature model, which help get corresponding user preferences. FM（Factorization Machine） predicts different types of user behavior, which is the basis of recommendation. FM has made full use of useful music features and user features information. The most important is that FM can simulate hidden factors of user behavior data to fill sparse matrix, and sparsity has little impact on prediction. Compared with traditional recommendation models, ming user interest from behavior data is feasible, experimental results also show that this method has good effect on music recommendation.

Key words: music recommendation, factorization machine, behavior prediction, data ming