Music Recommendation Method Research Articles

Context Aware Music Recommendation and Playlist Generation

There are many reasons people listen to music, and the type of music is largely determined by what the listener may be doing while they listen. For example, one may listen to one type of music while commuting, another while exercising, and yet another while relaxing. Without access to the physiological state of the user, current music recommendation methods rely on collaborative filtering - recommending music based on what other similar users listen to - and content based filtering - recommending songs based on their similarities to songs the user already prefers. With the rise in popularity of smart devices and activity trackers, physiological context can be a new channel to inform music recommendations. We propose deep learning solutions for context aware recommendation and playlist generation. Specifically, we use variational autoencoders (VAEs) to create a song embedding. We then explore multi-task multi-layer perceptrons (MLPs) and Gaussian mixture models to recommend songs based on context. We generate artificial user data to train and test our models in online learning and supervised learning settings.

An Improved Intelligent Machine Learning Approach to Music Recommendation Based on Big Data Techniques and DSO Algorithms

INTRODUCTION: In an effort to enhance the quality of user experience in using music services and improve the efficiency of music recommendation platforms, researching accurate and efficient music recommendation methods and constructing an accurate real-time online recommendation platform are the key points for the success of a high-quality music website platform.OBJECTIVES: To address the problems of incomplete signal feature capture, insufficient classification efficiency and poor generalization of current music recommendation methods.METHODS: Improve the deep confidence network to construct music recommendation algorithm by using big data and intelligent optimization algorithm. Firstly, music features are extracted by analyzing the principle of music recommendation algorithm, and evaluation indexes of music recommendation algorithm are proposed at the same time; then, combined with the deep sleep optimization algorithm, a music recommendation method based on improved deep confidence network is proposed; finally, the efficiency of the proposed method is verified through the analysis of simulation experiments.RESULTS: While meeting the real-time requirements, the proposed method improves the music recommendation accuracy, recall, and coverage.CONCLUSION: Solves the questions of incomplete signal feature capture, insufficient classification efficiency, and poor generalization of current music recommendation algorithms.

Cognitive mapping-based audiovisual and singing music therapy as a cross-intervention for college students’ anxiety

Abstract This paper explores the feasibility of music therapy in alleviating students’ anxiety symptoms by comparing the effects of different audiovisual singing music therapies on the regulation of students’ anxiety states. The method of emotion calculation and exponential function is used to quantify students’ emotions, combined with the emotion matrix to express the degree of change of students’ emotions, and the cognitive map of music preference is constructed according to the prediction of students’ emotional state. Using emotion similarity matrix to determine the degree of music and student emotion matching available. Music recommendation methods were utilized to push music to anxious student groups, and the students’ music inclination degree was derived from the data. The effects of classical music and light music on students’ anxiety were also analyzed. The results showed that the only two categories of students’ music style tendency degree > 0.5 under anxiety were light music and classical music, and the music tendency degree was categorized as 0.823 and 0.797. The students’ anxiety scores were significantly lower than those before the intervention after the music intervention, with a T-value of 2.52, p<0.05. The difference between the scores of the Sleep Quality Scale and the pre-intervention was insignificant. Lower than that of the control group but the difference was not significant p>0.05, which indicates that music therapy can effectively relieve students’ anxiety.

Study on improved personalised music recommendation method based on label information and recurrent neural network

Study on improved personalised music recommendation method based on label information and recurrent neural network

BTSAMA

To deal with the problems of occurring personalized music recommendation methods, for instance, low explanation, low accuracy of recommendation, and difficulty extracting information effectively, a personalized music recommendation method combining TextCNN and attention is proposed. Firstly, TextCNN model and BERT are combined to capture local music continuous features. Secondly, self-attention is introduced to solve the remaining omitted non-continuous features that are not paid attention by TextCNN. Finally, multi-headed attention mechanism is used to get features of hotspot music and user's interest music, and cascading fusion method is used to achieve click prediction. Experimentally, the proposed model can effectively recommend personalized music, its MAE values on FMA and GTZAN datasets are 0.156 and 0.146, respectively, improving by at least 6.6% and 3.3% compared to other comparative models. And its RMSE result values on the FMA and GTZAN datasets are 0.185 and 0.164, respectively, improving by at least 12.4% and 5.2% compared to other comparative models.

Music Recommendation System Based on Machine Learning

Machine learning is a hot topic of research in recent years and has a very wide range of applications in the fields of banking, insurance, transportation, biology, and medicine. A recommendation system is an information filtering system that uses algorithms to pinpoint a user's biased preferences by filtering redundant information from a large dataset and recommending new relevant content to the target user that he or she may like. In this paper, we categorize and outline several mainstream music recommendation methods: content-based and collaborative filtering recommendations, as well as weighted hybrid models of both.

Research on long and short term music preference recommendation method integrating music emotional attention

Research on long and short term music preference recommendation method integrating music emotional attention

Research on long- and short-term music preference recommendation method integrating music emotional attention

Research on long- and short-term music preference recommendation method integrating music emotional attention

A personalised recommendation method of pop music based on machine learning

In order to enhance the satisfaction of pop music personalised recommendation and improve the accuracy and efficiency of pop music personalised recommendation, a pop music personalised recommendation method based on machine learning is proposed. Firstly, the relevant theories of machine learning and short-term and long-term memory artificial neural networks are studied, and then the popular music word vector is extracted by using softmax function, and the collaborative filtering algorithm with weighting factor is introduced to calculate the similarity of popular music word vector. Finally, based on the LSTM network, a pop music personalised recommendation model is constructed to realise pop music personalised recommendation. Experiments show that the method proposed in this paper has a personalised recommendation satisfaction index of 97.8% for pop music, the recommendation time is only 19.4s, and the average value of MAPE and RMSE are only 0.037 and 0.039 respectively. The recommendation accuracy, satisfaction and efficiency are high, and the design purpose can be achieved.

A method for personalised music recommendation based on emotional multi-label

In this paper, a personalised music recommendation method based on emotion multi-label was proposed. First is the analysis of music emotion and music emotional label, then, the principal component analysis method is used to reduce the dimension to process the music features and complete the preprocessing. Secondly, construct the music emotion multi-label, and combine the cosine method to calculate the emotional multi-label similarity. Finally, the interest degree of emotional multi-label is calculated to obtain the user's interest degree of music resources, and the personalised recommendation method is optimised to realise the personalised recommendation of music. Experimental results show that the average coverage rate of personalised music recommendation of the proposed method is as high as 99.5%, the accuracy is 98.3%, and the recommendation time of 500 music items is only 18.9 s. Therefore, the recommendation effect of the proposed method is good, the accuracy of personalised music recommendation is improved, and the recommendation time is shortened.

An emotion-based personalized music recommendation framework for emotion improvement

Music has a close relationship with people's emotion and mental status. Music recommendation has both economic and social benefits. Unfortunately, most existing music recommendation methods were constructed based on genre features (e.g., style and album), which cannot meet the emotional needs of listeners. Furthermore, the “filter bubble” effect may make the situation even worse, when a user seeks music for emotional support. In this study, we designed a novel emotion-based personalized music recommendation framework to meet users’ emotional needs and help improve their mental status. In our framework, we designed a LSTM-based model to select the most suitable music based on users’ mood in previous period and current emotion stimulus. A care factor was used to adjust the results so that users’ mental status could be improved by the recommendation. The empirical experiments and user study showed that the recommendations of our novel framework are precise and helpful for users.

Automatic Recommendation of Online Music Tracks Based on Deep Learning

It is one of the main goals of personalized music recommendation system that how to accurately recommend the songs in line with users’ interests in the huge music library. In view of the above problems, this study proposes a personalized music recommendation method based on convolutional neural network. First, this study defines a training set containing potential musical characteristics and, combined with the depth of the belief network, design a music information prediction model and the research in the music-type classification method with different dimensions. Based on selecting four different kinds of music information better describing the underlying characteristics of 40D feature vector to every song music composition, the music feature set is constructed. Then, the CNN (convolutional neural network), which is widely used in audio field, is used as the music information prediction model, and its structural parameters are redesigned to complete the multidimensional music information prediction, which solves the cold start problem to some extent.

Study on improved personalised music recommendation method based on label information and recurrent neural network

Study on improved personalised music recommendation method based on label information and recurrent neural network

Paper] Deep Reinforcement Learning-based Music Recommendation with Knowledge Graph Using Acoustic Features

In this study, we propose a new deep reinforcement learning-based music recommendation method with knowledge graphs. With the rapid development of Web services, music-related content posted on platforms, such as YouTube, is increasing dramatically. Conventional recommendation methods based on knowledge graphs have struggled with the cold-start problem caused by a lack of user preference information. The proposed method can solve this problem by introducing acoustic feature edges in the constructed knowledge graph. Furthermore, we realize efficient search using a deep reinforcement learning algorithm on a dense knowledge graph introducing acoustic feature-based edges. The proposed method can make appropriate recommendations even with a small amount of user preference information by learning the optimal action of the agent. We confirm the effectiveness of the proposed method by comparing our method with several conventional and state-of-the-art recommendation methods.

A Novel Emotion-Aware Hybrid Music Recommendation Method Using Deep Neural Network

Emotion-aware music recommendations has gained increasing attention in recent years, as music comes with the ability to regulate human emotions. Exploiting emotional information has the potential to improve recommendation performances. However, conventional studies identified emotion as discrete representations, and could not predict users’ emotional states at time points when no user activity data exists, let alone the awareness of the influences posed by social events. In this study, we proposed an emotion-aware music recommendation method using deep neural networks (emoMR). We modeled a representation of music emotion using low-level audio features and music metadata, model the users’ emotion states using an artificial emotion generation model with endogenous factors exogenous factors capable of expressing the influences posed by events on emotions. The two models were trained using a designed deep neural network architecture (emoDNN) to predict the music emotions for the music and the music emotion preferences for the users in a continuous form. Based on the models, we proposed a hybrid approach of combining content-based and collaborative filtering for generating emotion-aware music recommendations. Experiment results show that emoMR performs better in the metrics of Precision, Recall, F1, and HitRate than the other baseline algorithms. We also tested the performance of emoMR on two major events (the death of Yuan Longping and the Coronavirus Disease 2019 (COVID-19) cases in Zhejiang). Results show that emoMR takes advantage of event information and outperforms other baseline algorithms.

CAME: Content- and Context-Aware Music Embedding for Recommendation.

Traditional recommendation methods suffer from limited performance, which can be addressed by incorporating abundant auxiliary/side information. This article focuses on a personalized music recommender system that incorporates rich content and context data in a unified and adaptive way to address the abovementioned problems. The content information includes music textual content, such as metadata, tags, and lyrics, and the context data incorporate users' behaviors, including music listening records, music playing sequences, and sessions. Specifically, a heterogeneous information network (HIN) is first presented to incorporate different kinds of content and context data. Then, a novel method called content- and context-aware music embedding (CAME) is proposed to obtain the low-dimension dense real-valued feature representations (embeddings) of music pieces from HIN. Especially, one music piece generally highlights different aspects when interacting with various neighbors, and it should have different representations separately. CAME seamlessly combines deep learning techniques, including convolutional neural networks and attention mechanisms, with the embedding model to capture the intrinsic features of music pieces as well as their dynamic relevance and interactions adaptively. Finally, we further infer users' general musical preferences as well as their contextual preferences for music and propose a content- and context-aware music recommendation method. Comprehensive experiments as well as quantitative and qualitative evaluations have been performed on real-world music data sets, and the results show that the proposed recommendation approach outperforms state-of-the-art baselines and is able to handle sparse data effectively.

Retracted] Music Recommendation Algorithm Based on Multidimensional Time‐Series Model Analysis

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.

A Deep Music Recommendation Method Based on Human Motion Analysis

Despite its applications in automatic video editing and automatic music composition, the problem of music recommendation from dance motions has seldom been explored. In order to solve this problem, this work proposes a deep music recommendation algorithm based on dance motion analysis and evaluate it through quantitative measures. For quantitative evaluation, this work implements a LSTM-AE based music recommendation method which learns the correspondences between motion and music. In experiments, the two methodologies are compared and the motion analysis based methods outperform their rival by large margins. This work also proposes a quantitative measure of accurately recommended music genre. The proposed motion analysis based method achieves a recommendation accuracy of 91.3% using late fusion of joint and limb features.

Bipartite graph link prediction method with homogeneous nodes similarity for music recommendation

Through analyzing users’ listening records, personalized music recommendation can not only help users find interesting music, but also help related enterprises improve user loyalty. This paper proposes an improved music recommendation method based on bipartite graph link prediction with homogeneous nodes similarity. Firstly, users’ music preference relations are expressed as positive and negative binary preference relations by the Complex Representation-based Link Prediction (CORLP) method, which improves the limitation of traditional recommendation method that can only represent unary preference relations. Secondly, the new method improves the CORLP method by attribute extraction and similarity calculation of homogeneous nodes including user nodes and music nodes. Thirdly, a new dataset based on the practical data from Shrimp Music Community is collected for facilitating the music recommendation task. The first-class music genres and second-class music genres of users are extracted by web crawling technology to calculate the similarity between user nodes. The rhythm and tempo are extracted by open source software to calculate the similarity between music nodes. Finally, the Top-N experiment is used to prove the performance of the proposed method compared with CORLP. In addition, the results reveal several new findings. Firstly, performance is significantly improved when the homogeneous nodes similarity is taken into account. Secondly, recommendation method with user nodes similarity shows a better performance compared with recommendation method with music nodes similarity.

融合LSTM和注意力机制的音乐分类推荐方法

融合LSTM和注意力机制的音乐分类推荐方法