Music Recommendation Research Articles

Intelligent Recommendation Model of Contemporary Pop Music Based on Knowledge Map.

With the advent of the era of big data, the rise of Web2.0 completely subverts the traditional Internet model and becomes the trend of today's information age. Simultaneously, massive amounts of data and information have infiltrated various Internet companies, resulting in an increase in the problem of information overload. In the online world, learning how to quickly and accurately select the parts we are interested in from a variety of data has become a hot topic. Intelligent music recommendation has become a current research hotspot in music services as a viable solution to the problem of information overload in the digital music field. On the basis of precedents, this paper examines the characteristics of music in a comprehensive and detailed manner. A knowledge graph-based intelligent recommendation algorithm for contemporary popular music is proposed. User-defined tags are described as the free genes of music in this paper, making it easier to analyze user behavior and tap into user interests. It has been confirmed that this algorithm's recommendation quality is relatively high, and it offers a new development path for improving the speed of searching for health information services.

Considering emotions and contextual factors in music recommendation: a systematic literature review

Considering emotions and contextual factors in music recommendation: a systematic literature review

Design of a Music Recommendation Model on the Basis of Multilayer Attention Representation

Current music recommendation systems can explore the general relationship between the users and songs to recommend music to the users; however, they cannot distinguish the different preferences of different users for the same song. For example, a user may like a song because of the singer, while another user will like it not for the singer but just because of the composition of the song or its melody. A recommender system that knows this difference would be more effective in recommending music to the users. To this end, this paper proposes a music recommendation model based on multilayer attention representation, which learns song representations from multidimensions using user-attribute information and song content information, and mines the preference relationship between users and songs. In order to distinguish the differences in user preferences for multidomain features of songs, a feature-dependent attention network is designed; in order to distinguish the differences in user preferences for different historical behaviors and to explore the temporal dependence of user behaviors, a song-dependent attention network is designed. Finally, the SoftMax function is used to calculate the distribution of users’ preferences for candidate songs and is used to generate recommendations. The experimental results on 30Music and MIGU datasets show that the proposed model achieves significant improvement in recall and MRR compared with the current recommendation models.

Music Recommendation System Driven by Facial Expression Recognition

Music Recommendation System Driven by Facial Expression Recognition

Improved self‐attentive Musical Instrument Digital Interface content‐based music recommendation system

AbstractAutomatic music recommendation is an open research problem that has seen much work in recent years. A common and successful music recommendation approach is collaborative filtering, which has worked well in this domain. One major drawback of this method is that it suffers from a cold‐start problem, and it requires a lot of user‐personalized information. It is an ineffective mechanism for recommending new and unpopular songs as well as for new users. In this article, we report a hybrid methodology that uses the song's content information. We use MIDI (Musical Instrument Digital Interface) content data, a compressed version of an audio song that contains digital information about a song and is machine‐readable. We describe a model called MSA‐SRec (MIDI Based Self Attentive Sequential Music Recommendation), a latent factor‐based self‐attentive deep learning model that uses a substantial amount of sequential information as content information of the song for recommendation generation. We use MIDI data of a song that is under‐explored content information for music recommendation. We show that using MIDI as content data with user and item latent vector produces reasonable recommendations. We also demonstrate that using MIDI over other music metadata performs better with various state‐of‐the‐art models of recommendation systems.

“Knowing me, knowing you”: personalized explanations for a music recommender system

Due to the prominent role of recommender systems in our daily lives, it is increasingly important to inform users why certain items are recommended and personalize these explanations to the user. In this study, we explored how explanations in a music recommender system should be designed to fit the preference of different personal characteristics. More specifically, we investigated three personal characteristics that influence the perception of explanations in music recommender system interfaces: need for cognition, musical sophistication, and openness. For each of these personal characteristics, we designed explanations for users with lower and higher levels of the personal characteristic. Afterward, we conducted for each personal characteristic a within-subject user study in which we compared the two explanations. Based on the results of these user studies, we provide design suggestions to adapt explanations to different levels of these three personal characteristics. In general, we suggest providing explanations up-front for all recommendations at once. For users low in need for cognition, displaying these explanations must be optional. To support users with low musical sophistication, we suggest providing brief explanations that do not require domain knowledge. For users with low openness, we suggest providing explanations with a lower number of explanation elements.

‘Don’t mess with my algorithm’: Exploring the relationship between listeners and automated curation and recommendation on music streaming services

Given access to huge online collections of music on streaming platforms such as Spotify or Apple Music, users have become increasingly reliant on algorithmic recommender systems and automated curation and discovery features to find and curate music. Based on participant observation and semi-structured interviews with 15 active users of music streaming services, this article critically examines the user experience of music recommendation and streaming, seeking to understand how listeners interact with and experience these systems, and asking how recommendation and curation features define their use in a new and changing landscape of music consumption and discovery. This paper argues that through daily interactions with algorithmic features and curation, listeners build complex socio-technical relationships with these algorithmic systems, involving human-like factors such as trust, betrayal and intimacy. This article is significant as it positions music recommender systems as active agents in shaping music listening habits and the individual tastes of users.

Exploiting MUSIC model to solve cold-start user problem in content-based music recommender systems

The new user cold-start problem is a grand challenge in content-based music recommender systems. This happens when the systems do not have sufficient information regarding the user’s preferences. Towards solving this problem, in this study, a rating prediction framework is proposed. The proposed framework allows the systems to predict the user’s rating scores for unrated musical pieces, by which good recommendations can be generated. The core idea here is to leverage the so-called MUSIC model, i.e., a five-factor musical preference model, which is characterized by Mellow, Unpretentious, Sophisticated, Intense, and Contemporary as the user’s musical preference profiles. When a user newly joins the systems, the first five-factor musical preference profile is established based on the user’s age and brain type information which is extracted from questionnaires. When the user experiences the systems for a certain period, his/her rating scores for experienced musical pieces are utilized for generating the second five-factor musical preference profile. The recommendations are then provided based on the rating scores predicted from a non-linear combination of these two five-factor musical preference profiles. The results demonstrated the effectiveness of the five-factor musical preference in alleviating the new user cold-start problem. In addition, the proposed method can potentially provide high-quality recommendations.

Variational Fuzzy Neural Network Algorithm for Music Intelligence Marketing Strategy Optimization.

In this paper, we use a variational fuzzy neural network algorithm to conduct an in-depth analysis and research on the optimization of music intelligent marketing strategy. The music recommendation system proposed in this paper includes a user modelling module, audio feature extraction module, and recommendation algorithm module. The basic idea of the recommendation algorithm is as follows: firstly, the historical behavioural information of music users is collected, and the user preference model is constructed by using the method of matrix decomposition of the hidden semantic model; then, the audio resources in the system are preprocessed and the spectrum map that can represent the music features is extracted; the similarity between the user's preferred features and the music potential features are calculated to generate recommendations for the target user. The user-music dataset for model training and testing is constructed in-house, and the network model structure used for system experiments is designed based on a typical convolutional neural network model, while the model training tuning parameters are compared and selected. Finally, the model is trained and tested in this paper, and the system is evaluated in terms of both prediction rating accuracy and recommendation list generation accuracy using root mean square error, accuracy, recall, and F1 value as recommendation quality evaluation metrics. The experimental results show that the recommendation algorithm in this paper has certain feasibility and effectiveness. Compared with other traditional music recommendation algorithms, this paper makes full use of the powerful advantage of deep neural networks to automatically extract features and obtain higher-level music feature representations from the audio content, while incorporating the historical behavioural information of user interactions with music, which can effectively alleviate the problems such as cold start in recommendation systems.

Modeling Sequential Listening Behaviors With Attentive Temporal Point Process for Next and Next New Music Recommendation

Recommender systems, which aim to provide personalized suggestions for users, have proven to be an effective approach to cope with the information overload problem existing in many online applications and services. In this paper, we target two specific sequential recommendation tasks, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">next music recommendation and next new music recommendation</i> , to predict the next (new) music piece that users would like based on their historical listening records. In current music recommender systems, various kinds of auxiliary/side information, e.g., item contents and users’ contexts, have been taken into account to facilitate user/item preference modeling and have yielded comparable performance improvement. Despite the gained benefits, it is still a challenging and important problem to fully exploit sequential music listening records due to the complexity and diversity of interactions and temporal contexts among users and music, as well as the dynamics of users’ preferences. To this end, this paper proposes a novel <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> ttentive <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</u> emporal <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u> oint <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u> rocess ( <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATPP</small> ) approach for sequential music recommendation, which is mainly composed of a temporal point process model and an attention mechanism. Our <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATPP</small> can effectively capture the long- and short-term preferences from the sequential behaviors of users for sequential music recommendation. Specifically, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATPP</small> is able to discover the complex sequential patterns from the interaction between users and music with the temporal point process, as well as model the dynamic impact of historical music listening records on next (new) music pieces adaptively with an attention mechanism. Comprehensive experiments on four real-world music datasets demonstrate that the proposed approach <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATPP</small> outperforms state-of-the-art baselines in both next and next new music recommendation tasks.

Graph-Based Attentive Sequential Model With Metadata for Music Recommendation

Massive music data and diverse listening behaviors have caused great difficulties for existing methods in user-personalized recommendation scenarios. Most previous music recommendation models extract features from temporal relationships among sequential listening records and ignore the utilization of additional information, such as music’s singer and album. Especially, a piece of music is commonly created by a specific musician and belongs to a particular album. Singer and album information, regarded as music metadata, can be utilized as important auxiliary information among different music pieces and may considerably influence the user’s choices of music. In this paper, we focus on the music sequential recommendation task with the consideration of the additional information and propose a novel Graph-based Attentive Sequential model with Metadata (GASM), which incorporates metadata to enrich music representations and effectively mine the user’s listening behavior patterns. Specifically, we first use a directed listening graph to model the relations between various kinds of nodes (user, music, singer, album) and then adopt the graph neural networks to learn their latent representation vectors. After that, we decompose the user’s preference for music into long-term, short-term and dynamic components with personalized attention networks. Finally, GASM integrates three types of preferences to predict the next (new) music in accordance with the user’s music taste. Extensive experiments have been conducted on three real-world datasets, and the results show that the proposed method GASM achieves better performance than baselines.

Construction and implementation of music recommendation model utilising deep learning artificial neural network and mobile edge computing

The purpose is to better develop China's music industry, facilitate users' online music works queries, and encourage the development of excellent music works. The music recommendation system is analysed, and a new music recommendation system is constructed based on the combination of the hybrid Deep Learning (DL) Artificial Neural Network (ANN) and Mobile Edge Computing (MEC) technologies. First, the principle of MEC is analysed. Then, the combination of Deep Learning Artificial Neural Network (DLANN) technology and MEC technology is discussed. Afterward, DLANN and MEC are fused to implement the music recommendation model, and the proposed model is evaluated through experiment. The results show that the combination of DLANN and MEC technologies can increase the DL efficiency of the computer, the storage capacity, and the overall efficiency of the servers. This proves the feasibility of the proposed music recommendation system. The user satisfaction of the proposed music recommendation system exceeds all mainstream music recommendation algorithms on the market. Thus, the research can provide a reference for the improvement of the music recommendation algorithm in the future and is of great significance. The proposed music recommendation system outperforms most of the current music recommendation systems on the market.

A review on music recommendations based on facial expression

Deciding which music to listen to from the huge collection of existing options is often confusing. Depending on the user's mood, multiple suggestion frames are available for topics such as music, food, and shopping. The main purpose of this music recommendation system is to provide users with suggestions based on users' tastes. By analysing the user's facial expressions and emotions, it is possible to understand the user's current emotional and psychological state. Music and video are areas where there is a great opportunity to offer customers a wide range of choices, taking into account customer preferences and recorded information. It is well known that people use facial expressions to more clearly expresses what they want to say and the context of the words. More than 60% of the users believe that the song library has too many songs at any given time to find the one that needs to be played. Developing a recommendation system could help users decide which music to listen to and reduce stress levels. Users do not have to waste time searching and searching for songs; it will recognize the track that best fits the user's mood and present the songs to the user according to the user's mood. Music plays a role in emotions, which in turn affects mood. Books, movies, and Television (TV) shows are a few more means, but unlike these, music conveys its message in pure moments. It can help us when people feel low. When people listen to sad songs, their mood tends to drop. When they listen to happy songs, it makes them feel happier. This music recommendation model will mainly work to improve the mood of the user by providing a detection track for the user's facial expression and recommending the preferred song according to the user's expression. User images are captured using webcams. A user's picture is taken, and depending on the user's mood or feeling, appropriate songs are displayed from the user's playlist to meet the user's requirements.

Content-Based Music Recommendation Using Non-Stationary Bayesian Reinforcement Learning

This paper presents a music recommendation system for the offline libraries of songs that employs the concepts of reinforcement learning to obtain satisfactory recommendations based on the various considered content-based parameters. In order to obtain insights about the effectiveness of the generated recommendations, parallel instances of single-play multi-arm bandit algorithms are maintained. In conjunction to this, the concepts of Bayesian learning are considered to model the user preferences, by assuming the environment’s reward generating process to be non-stationary and stochastic. The system is designed to be simple, easy to implement, and at-par with the user satisfaction, within the bounds of the input data capabilities.

Content-Based Music Recommendation Using Non-Stationary Bayesian Reinforcement Learning

This paper presents a music recommendation system for the offline libraries of songs that employs the concepts of reinforcement learning to obtain satisfactory recommendations based on the various considered content-based parameters. In order to obtain insights about the effectiveness of the generated recommendations, parallel instances of single-play multi-arm bandit algorithms are maintained. In conjunction to this, the concepts of Bayesian learning are considered to model the user preferences, by assuming the environment’s reward generating process to be non-stationary and stochastic. The system is designed to be simple, easy to implement, and at-par with the user satisfaction, within the bounds of the input data capabilities.

Construction and implementation of music recommendation model utilising deep learning artificial neural network and mobile edge computing

Construction and implementation of music recommendation model utilising deep learning artificial neural network and mobile edge computing

Music Feature Extraction And Recommendation Using CNN Algorithm

In this age of technological advancements, it has become considerably easier for an individual to access a variety of music from a significant number of sources. Today, there are a multitude of songs of varying diversity available to users. Therefore, it becomes difficult for users to manually discover new music that may suit their liking. Thus arises the need for a system that will help the music streaming applications to recommend new music to their users that will befit their music taste, based on some predetermined criteria. With the ever-expanding user and song database, the system must also be dynamic and its recommendations must be up-to-date and accurate. Therefore, there is a strong demand for a well-qualified music recommendation system. The proposed system focuses of technical features of audio. The main purpose of this systems is to classify songs in different genre using Deep Learning Algorithm. There are two main approaches for implementing these system, viz, Feature Extraction and Content Based Filtering.

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.

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.