This paper proposes a personalized music recommendation method based on multidimensional time-series analysis, which can improve the effect of music recommendation by using user’s midterm behavior reasonably. This method uses the theme model to express each song as the probability of belonging to several hidden themes, then models the user’s behavior as multidimensional time series, and analyzes the series so as to better predict the use of music users’ behavior preference and give reasonable recommendations. Then, a music recommendation method is proposed, which integrates the long-term, medium-term, and real-time behaviors of users and considers the dynamic adjustment of the influence weight of the three behaviors so as to further improve the effect of music recommendation by adopting the advanced long short time memory (LSTM) technology. Through the implementation of the prototype system, the feasibility of the proposed method is preliminarily verified.

中文翻译：

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基于多维时间序列模型分析的音乐推荐算法

提出了一种基于多维时间序列分析的个性化音乐推荐方法，可以通过合理地利用用户的中期行为来提高音乐推荐的效果。该方法使用主题模型将每首歌曲表达为属于多个隐藏主题的概率，然后将用户的行为建模为多维时间序列，并对该序列进行分析，从而更好地预测音乐用户的行为偏好使用并给出合理的选择。建议。然后，提出了一种音乐推荐方法，该方法融合了用户的长期，中期和实时行为，并考虑了对这三种行为的影响权重的动态调整，以进一步提高音乐推荐的效果。通过采用先进的长短时记忆（LSTM）技术。