Music Recommendation Algorithm Research Articles

Context-Aware Music Recommendation Algorithm Combining Classification and Collaborative Filtering

As an effective solution to the problem of information overload, personalized recommendations have received widespread attention in the music field. A context-aware music recommendation algorithm combining classification and collaborative filtering is proposed based on user context information. Firstly, the similarity analysis of the user situation is carried out. A preliminary list of recommended songs is obtained by collaborative filtering. The machine learning method is used to classify music in different scenes to get the preferences of music types in different situations. Finally, the recommendation list obtained by collaborative filtering is combined with the music type preference obtained by the classification model and personalized music recommendations for users in different situations. This algorithm not only effectively reduces the complexity of the recommendation process. Experiments show that the proposed algorithm can effectively improve the accuracy of users' music recommendations.

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.

A Research on Machine Learning Methods in a Healthcare Management Music System for Recommendations

Abstract: One of the best treatments for illnesses is music, which accelerates healing for patients. Music stimulates the patient's emotional thinking, helping them to overcome their illness and become psychologically well. This study examines the many kinds of machine learning techniques and how they are used to music recommendations for healthcare management. Both supervised and unsupervised machine learning approaches, as well as their subcategories, are covered. According to study, music therapy enhances people's quality of life while gradually lessening the negative impacts of illness on the body. A strong recommendation system combined with music therapy is a powerful tool for treating people's emotional and psychological problems.One crucial metric used to assess the efficacy of music recommendation algorithms is classification accuracy. In this survey, music recommendations for patient care management are made using machine learning algorithms.

Music recommendation algorithms based on knowledge graph and multi-task feature learning

During music recommendation scenarios, sparsity and cold start problems are inevitable. Auxiliary information has been utilized in music recommendation algorithms to provide users with more accurate music recommendation results. This study proposes an end-to-end framework, MMSS_MKR, that uses a knowledge graph as a source of auxiliary information to serve the information obtained from it to the recommendation module. The framework exploits Cross & Compression Units to bridge the knowledge graph embedding task with recommendation task modules. We can obtain more realistic triple information and exclude false triple information as much as possible, because our model obtains triple information through the music knowledge graph, and the information obtained through the recommendation module is used to determine the truth of the triple information; thus, the knowledge graph embedding task is used to perform the recommendation task. In the recommendation module, multiple predictions are adopted to predict the recommendation accuracy. In the knowledge graph embedding module, multiple calculations are used to calculate the score. Finally, the loss function of the model is improved to help us to obtain more useful information for music recommendations. The MMSS_MKR model achieved significant improvements in music recommendations compared with many existing recommendation models.

The Practice and Application of Philosophical Thought of Music Education in the Information Age in the Education of College Music Curriculum

Abstract Taking the philosophical view of music education as an entry point, this paper investigates the relevance of music education and philosophical thought from three aspects, focusing on the compatible philosophical view of music. Based on the random forest algorithm under machine learning, the music data samples are classified, and the algorithm is chosen to be optimized and improved when the dimension of music features is high. Using the extreme gradient boosting algorithm, the model is penalized to avoid the phenomenon of overfitting during the operation process. The RF-XGBoost hybrid music recommendation algorithm is proposed to help students learn personalized music by combining the two models mentioned above. Utilize ICT integration technology to combine music education and information technology in order to create the M-ICTPACK model. Create music simulation experiments to evaluate the impact of the music recommendation model proposed in this paper. Based on the idea of music education and philosophy, design music teaching content that meets the needs of students in colleges and universities and analyze its impact. The results show that in the music simulation recommendation effect, the smallest gap between the recommended musicians and the type of interest of the students is only 0.00001. The same student scores differently in music skills in different periods of music learning, and after 9 months of learning, except for the learning interest score of 79.365, the scores of all other aspects are more than 80. This paper proposes more effective philosophical ideas for music education in music curriculum education.

Music Therapy Music Selection Based on Big Data Analysis

Abstract Music, as a carrier of emotional sustenance, can not only achieve spiritual resonance in the process of listening but also reflect the vitality of life in the rhythm of music. As an effective relief method, music intervention has far-reaching significance in the development of psychotherapy. In the practice of music therapy, music selection is particularly important, and the rationality and rigor of music selection directly affect the therapeutic effect. The process of music selection in music therapy is in common with Internet music recommendation, so it has theoretical and practical value to apply personalized music recommendation algorithms to music therapy. In this paper, driven by big data, a music recommendation model based on an improved collaborative filtering (CF) algorithm is proposed, which combines the psychological adjustment of users’ music preferences and different music rhythm features to select music for music therapy and provides theoretical support for music therapy selection. The results show that the construction of music resources for music therapy based on an improved CF algorithm can greatly improve the music selection process of music therapy.

Convolutional neural network and recommendation algorithm for the new model of college music education

With the rapid development of the field of artificial intelligence, we expect that AI+ music can produce a new music education model to help efficient college students improve their personal music level. In this paper, we design a convolutional neural network music recommendation system, including a user modeling module, audio feature extraction module recommendation algorithm module, etc., which can model students' music preferences to generate Top recommendations for target users. It is helpful for teachers to timely and dynamically grasp the types of music that students like. Experiments show that the proposed method has certain feasibility and effectiveness. Compared with other traditional music recommendation algorithms, we can make full use of the powerful advantages of deep neural network automatic feature extraction and integrate the historical behavior information of users' interaction with music.

System Construction of Intercity Culture and Xinjiang Music Integration in the Context of “One Belt, One Road” Based on SCP Model

Abstract Music content needs to draw nutrients from excellent regional culture, and excellent regional culture needs to be carried forward through music, so the integration of intercity culture and Xinjiang music is in line with the development and inheritance of music culture. This paper briefly introduces the analysis model of the SCP paradigm and the music recommendation algorithm improved by K-Means and Canopy clustering algorithms and analyzes the integration of intercity culture and Xinjiang music in the context of “One Belt One Road” based on the SCP model and the improved algorithm. The analysis shows that music lessons incorporating intercultural culture account for about 25% of the lesson time in Xinjiang’s primary and secondary school music curriculum, and in 2013, the content of Xinjiang’s local music culture at the junior high school level accounts for more than 35% of the total lesson time in the music curriculum. Intercultural culture and Xinjiang music are gradually merging, and music education will play a large role in further educational reform in terms of cultural transmission and integration.

Retracted: Hybrid Music Recommendation Algorithm Based on Music Gene and Improved Knowledge Graph

Retracted: Hybrid Music Recommendation Algorithm Based on Music Gene and Improved Knowledge Graph

A Study on Emotion Analysis and Music Recommendation Using Transfer Learning

As more and more people access and consume music through streaming platforms and digital services, music recommendation has grown in importance within the music industry. Given the abundance of music at our disposal, music recommendation algorithms are essential for guiding users toward new music and for creating individualized listening experiences. People frequently seek out music that fits their current emotional state or desired emotional state, which means that emotions can have a big impact on music recommendations. Emotions can be taken into account by music recommendation algorithms when deciding which songs or playlists to recommend to listeners. Face expressions are frequently used to gauge a person's mood. By using a webcam or any other external device, recognizable facial traits can now be extracted as inputs thanks to modern technology. Transfer learning is a method that is increasingly in demand for enhancing emotion recognition and music recommendation systems in the modern world. Transfer learning has evolved into a potent method for utilizing prior knowledge to enhance model performance and lessen the requirement for massive volumes of labeled data as a result of the data explosion and the availability of big pre-trained models. Hence, the objective of this study is to understand how transfer learning impacts the accuracy of detecting emotions from facial expressions and how the music recommendations can be personalized based on the detected emotions. This study aims at recommending songs by detecting the facial expressions of users using the FER2013 dataset for emotion recognition which is further extended by adding own images to the categories in the dataset from Google. A basic CNN, finetuned pre-trained ResNet50V2, finetuned pre-trained VGG16, and finetuned pre-trained EfficientNet50 B0 are trained on the dataset for emotion detection and compared. The music recommendation system is developed using the Spotify songs dataset extracted using Spotify web API. It uses k-means clustering for grouping tracks based on emotions and getting song recommendations based on the emotion predictions using finetuned ResNet50-V2 model with the highest training accuracy of 77.16% and validation accuracy of 69.04%. The findings reveal that using a transfer learning approach may effectively identify emotions from facial expressions and can have a potential impact on recommending music. It improves duties related to music recommendations and might be a useful method for assisting users in finding new music that fits the intended emotional state.

MULTI-SOURCE AND HETEROGENEOUS ONLINE MUSIC EDUCATION MECHANISM: AN ARTIFICIAL INTELLIGENCE-DRIVEN APPROACH

In order to solve the challenges brought by multi-source and cross-domain scenarios to online music education, this paper designs an online music education system based on advanced artificial intelligence technology, which can provide personalized learning course resource recommendations for music online learners. The system includes four layers, consisting of user interface layer, application module layer, function module layer and data storage layer. At the application module level, this paper proposes a music recommendation algorithm based on a personalized multimodal network model. The recommendation algorithm performs music information retrieval (MIR) based on the similarity judgment of the contour of music pitch and the overall change, and constructs a multimodal network model based on the user’s preference for resources to achieve personalized music recommendation. This paper crawls more than one million music score data from a well-known music platform database in China to establish a dataset to evaluate the performance of this method. The comparison results with three existing works show that the method proposed in this paper has good performance and can provide users with suitable music recommendations. The artificial intelligence technology-driven online music education mechanism proposed in this paper has good prospects.

Music Recommendation Based on “User-Points-Music” Cascade Model and Time Attenuation Analysis

Music has an increasing impact on people’s daily lives, and a sterling music recommendation algorithm can help users find their habitual music accurately. Recent research on music recommendation directly recommends the same type of music according to the specific music in the user’s historical favorite list. However, users’ behavior towards a certain cannot reflect the preference for this type of music and possibly provides music the listener dislikes. A recommendation model, MCTA, based on “User-Point-Music” structure is proposed. By clustering users’ historical behavior, different interest points are obtained to further recommend high-quality music under interest points. Furthermore, users’ interest points will decay over time. Combined with the number of music corresponding to each interest point and the liking degree of each music, a multi-interest point attenuation model is constructed. Based on the real data after desensitization and encoding, including 100,000 users and 12,028 pieces of music, a series of experimental results show that the effect of the proposed MCTA model has improved by seven percentage points in terms of accuracy compared with existing works. It came to the conclusion that the multi-interest point attenuation model can more accurately simulate the actual music consumption behavior of users and recommend music better.

Tchaikovsky Music Recommendation Algorithm Based on Deep Learning

In recent years, digital music is becoming more and more popular as mobile Internet and streaming media technology advance. Traditional music indexing technology mainly uses keywords to query. To find their favorite music, people must search through the vast amount of music available on the Internet nowadays, much like hunting for a needle in a haystack. In the era of mobile Internet, people’s pace of life is very fast. Devices can access the network anytime and anywhere. Users have the habit of listening to music in their daily work, study, or sports. Facing the vast music library, personalized music recommendation can help users quickly and accurately find music tracks that meet their interests, which is also the focus of current music recommendation technology. According to the characteristics of Tchaikovsky music, in this paper, we establish and build an approach that can understand situations and recommend by using the additional information of labels to describe Tchaikovsky music and realize a structure on this foundation. Through user involvement, the system can deliver services akin to network radio and complete the evaluation of the Tchaikovsky music recommendation algorithm’s efficacy.

Music Classification Method Using Big Data Feature Extraction and Neural Networks.

From the cassette era to the CD era to the digital music era, the quantity of music has grown rapidly. People cannot easily search for the desired music without classifying enormous music resources and developing a successful music retrieval system. By examining users' historical listening patterns for personalised recommendations, the music recommendation algorithm can lessen message fatigue for users and enhance user experience. Relying on manual labelling is how traditional music is classified. It would be inefficient and unrealistic to attempt to classify music using manual labelling in the age of big data. Feature extraction and neural networks are the tools employed in this paper. The model's parameters can be trained using conventional gradient descent techniques, and the model's trained convolution neural network can learn the image's features and finish the extraction and classification of the features. This algorithm is 12 percent superior to the conventional algorithm, according to the research in this paper. It has strong ability and is appropriate for widespread implementation with the same number of iterations.

Analysis of Sentiment and Personalised Recommendation in Musical Performance.

Music performance research is a comprehensive study of aspects such as emotional analysis and personalisation in music performance, which help to add richness and creativity to the art of music performance. The labels in this paper in collaborative annotation contain rich personalised descriptive information as well as item content information and can therefore be used to help provide better recommendations. The algorithm is based on bipartite graph node structure similarity and restarted random wandering. It analyses the connection between users, items, and tags in the music social network, firstly constructs the adjacency relationship between music and tags, obtains the music recommendation list and indirectly associated music collection, then fuses the results according to the proposed algorithm, and reorders them to obtain the final recommendation list, thus realising the personalised music recommendation algorithm. The experiments show that the proposed method can meet the personalised demand of users for music on this dataset.

Explainability in music recommender systems

AbstractThe most common way to listen to recorded music nowadays is via streaming platforms, which provide access to tens of millions of tracks. To assist users in effectively browsing these large catalogs, the integration of music recommender systems (MRSs) has become essential. Current real‐world MRSs are often quite complex and optimized for recommendation accuracy. They combine several building blocks based on collaborative filtering and content‐based recommendation. This complexity can hinder the ability to explain recommendations to end users, which is particularly important for recommendations perceived as unexpected or inappropriate. While pure recommendation performance often correlates with user satisfaction, explainability has a positive impact on other factors such as trust and forgiveness, which are ultimately essential to maintain user loyalty.In this article, we discuss how explainability can be addressed in the context of MRSs. We provide perspectives on how explainability could improve music recommendation algorithms and enhance user experience. First, we review common dimensions and goals of recommenders explainability and in general of eXplainable Artificial Intelligence (XAI), and elaborate on the extent to which these apply—or need to be adapted—to the specific characteristics of music consumption and recommendation. Then, we show how explainability components can be integrated within a MRS and in what form explanations can be provided. Since the evaluation of explanation quality is decoupled from pure accuracy‐based evaluation criteria, we also discuss requirements and strategies for evaluating explanations of music recommendations. Finally, we describe the current challenges for introducing explainability within a large‐scale industrial MRS and provide research perspectives.

Personalized Music Hybrid Recommendation Algorithms Fusing Gene Features

Aiming at the shortcomings of current music recommendation algorithms, such as low accuracy and poor timeliness, a personalized hybrid recommendation algorithm incorporating genetic features is proposed. The user-based collaborative filtering (UserCF) algorithm analyzes the degree of users’ preference for music genes. The improved neural matrix decomposition collaborative filtering (B-NCF) algorithm calculates the correlation between similar users and constructs the adjacency relationship between users. The results of the two algorithms are fused by using a weighted hybrid approach to generate the recommendation list. Finally, the hybrid recommendation model is built on the Spark platform. The paper’s traditional and hybrid recommendation algorithms are validated using the Yahoo Music dataset. The experimental results show that the advantages of the algorithm in this paper are more significant under the MAE and F1-measure indexes, and the recommendation accuracy and precision have been greatly improved; the hybrid algorithm can ensure the diversity of the recommended contents, the recommendation hit rate is higher, and the timeliness meets the demand of personalized music recommendation.

Hybrid Music Recommendation Algorithm Based on Music Gene and Improved Knowledge Graph

Combining music as a specific recommendation object, a hybrid recommendation algorithm based on music genes and improved knowledge graph is proposed for the traditional single recommendation algorithm that cannot effectively solve the accuracy problem in music recommendation. The algorithm first gives the recommendation pattern of music genes and gets the relevant recommendation results through the genetic preference analysis. After that, the algorithm in this paper utilizes item and user label information and knowledge graphs from two different domains to enrich and mine the potential information of users and items. In addition, deep learning method is applied to extract low-dimensional, abstract deep semantic features of users and items, based on which, score prediction is performed. The mixed-mode based recommendation addresses the drawbacks of these two recommendations and can adopt different weighting strategies in different situations. The advantages of music gene and knowledge graph-based recommendation algorithms are combined via this method. The experimental results indicate that the algorithm in this paper outperforms other existing recommendation algorithms.

Music information visualization and classical composers discovery: an application of network graphs, multidimensional scaling, and support vector machines

This article illustrates different information visualization techniques applied to a database of classical composers and visualizes both the macrocosm of the Common Practice Period and the microcosms of twentieth century classical music. It uses data on personal (composer-to-composer) musical influences to generate and analyze network graphs. Data on style influences and composers ‘ecological’ data are then combined to composer-to-composer musical influences to build a similarity/distance matrix, and a multidimensional scaling analysis is used to locate the relative position of composers on a map while preserving the pairwise distances. Finally, a support-vector machines algorithm is used to generate classification maps. This article falls into the realm of an experiment in music education, not musicology. The ultimate objective is to explore parts of the classical music heritage and stimulate interest in discovering composers. In an age offering either inculcation through lists of prescribed composers and compositions to explore, or music recommendation algorithms that automatically propose works to listen to next, the analysis illustrates an alternative path that might promote the active rather than passive discovery of composers and their music in a less restrictive way than inculcation through prescription.

Personalized Music Recommendation Algorithm Based on Spark Platform.

Aiming at the shortcomings of traditional recommendation algorithms in dealing with large-scale music data, such as low accuracy and poor real-time performance, a personalized recommendation algorithm based on the Spark platform is proposed. The algorithm is based on the Spark platform. The K-means clustering model between users and music is constructed using an AFSA (artificial fish swarm algorithm) to optimize the initial centroids of K-means to improve the clustering effect. Based on the scoring relationship between users and users and users and music attributes, the collaborative filtering algorithm is applied to calculate the correlation between users to achieve accurate recommendations. Finally, the performance of the designed recommendation model is validated by deploying the recommendation model on the Spark platform using the Yahoo Music dataset and online music platform dataset. The experimental results show that the use of improved AFSA can complete the optimization of K-means clustering centroids with good clustering results; combined with the distributed fast computing capability of Spark platform with multiple nodes, the recommendation accuracy has better performance than traditional recommendation algorithms; especially when dealing with large-scale music data, the recommendation accuracy and real-time performance are higher, which meet the current demand of personalized music recommendation.