Abstract
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.
Highlights
In recent years, with the rapid development of mobile Internet and smart phones, information is growing exponentially, and it leads to a serious problem of information overload [1]
In order to solve the problems existing in the literature and improve the recommendation effect by using the user’s midterm behavior reasonably, this paper proposes a music recommendation method based on multidimensional timeseries analysis. e theme model is used to represent songs as probability distribution composed of several hidden themes, and on this basis, the user’s behavior in the current session is represented as multidimensional time series. rough the analysis of the multidimensional time series, the method predicts the characteristics of the song that users may listen to and selects similar songs from the music library to recommend to users
Rough the analysis of the music recommendation method based on multidimensional time-series analysis, we can see that the datasets we need mainly include the song dataset containing label text information and the song dataset that users listen to in a certain session cycle
Summary
With the rapid development of mobile Internet and smart phones, information is growing exponentially, and it leads to a serious problem of information overload [1]. This paper believes that the future behavior of users is related to their long-term behavior, medium-term behavior, and immediate behavior, but there is no work to consider the influence and effect of these three aspects. In order to solve the problems existing in the literature and improve the recommendation effect by using the user’s midterm behavior reasonably, this paper proposes a music recommendation method based on multidimensional timeseries analysis. Rough the analysis of the multidimensional time series, the method predicts the characteristics of the song that users may listen to and selects similar songs from the music library to recommend to users.
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