Song Recommendation Research Articles

Review Paper for Emotion Based Song Recommendation System

The mood or attitude of the user can be determined by the shape of his face. These expressions can be extracted from the live camera feed of the system. In the field of computer vision and machine learning (ML), where machines are trained to recognize different human emotions or feelings, much research is being done. The human emotions can be perceived through the various techniques provided by machine learning. One such strategy is to integrate the CNN and Keras model, which produces a less trained model and simplifies ML integration. Music is often used to control emotions, especially to change bad moods, to improve energy, or to reduce tension. In addition, listening to the right music at the right time can contribute to good mental health.

Personalized Song Recommendation System Based on Vocal Characteristics

To find favorite songs among massive songs has become a difficult problem. The song recommendation algorithm makes personalized recommendations by analyzing user’s historical behavior, which can reduce user’s information fatigue and improve the user experience. This paper studies a personalized song recommendation algorithm based on vocal features. The specific work includes three parts. Firstly, the spectrum feature extraction and observe feature extraction of songs. The spectrum includes three types of features: time domain, frequency domain, and amplitude, which implicitly describe the rhythm, notes, and high-pitched or soothing properties of songs. Furthermore, automatic note recognition methods are explored as explicit classification features. The characteristic of this work is to use the comprehensive features of spectrum and musical notes as the classification basis. Secondly, based on song of convolutional neural network (CNN) classification, it sets different types of song classification. For the training of CNN, ELU, and ReLU, RMSProp and Adam were explored, and their performance and characteristics during training were compared. The classification methods were compared under the two configurations based on the spectrum as the classification basis and the comprehensive characteristic frequency of the spectrum and the note as the basis. Thirdly, a personalized song recommendation method was based on CNN classification. Also, the reasons why classification of CNN is not suitable for direct song recommendation are analyzed, and then, a recommendation method based on song fragment classification is proposed. A threshold model that can distinguish between pseudodiscrete and true-discrete is proposed to improve the accuracy of song classification.

Implementation of JIProlog on an Android-based Song Expert System to Provide Song Recommendations Based on 16 Human Personality Types

Songs can be enjoyed by someone both in sad and happy heart conditions. The genres of songs are very diverse according to the personality that a person has. Songs also always get attention in society. However, the problem often experienced by some people who rarely enjoy songs is that they don't know what songs suit their personality when they want to listen to music. The purpose of this research is to develop an Android-based Song Expert System application with the implementation of the Java Internet Prolog (JIProlog) library. The method used in designing this expert system is forward chaining; this method aims to browse data on a knowledge base logically. The results of this application will provide song recommendations based on 4 dimensions of personality, namely, dimension 1 (introvert/extrovert), dimension 2 (sensory/intuitive), dimension 3 (thinking/feeling), and dimension 4 (judging/perceiving). Of the 16 human personalities, each personality type will be given 3 song genres. So that users can choose a variety of songs that suit their personality. The test results show that the system successfully displays recommendations with the knowledge base, but the resulting song recommendations still have limitations. Hopefully, this song expert system can help someone get songs that match their personality and the condition of the heart that is being experienced.

Sequence-Based Explainable Hybrid Song Recommendation.

Despite advances in deep learning methods for song recommendation, most existing methods do not take advantage of the sequential nature of song content. In addition, there is a lack of methods that can explain their predictions using the content of recommended songs and only a few approaches can handle the item cold start problem. In this work, we propose a hybrid deep learning model that uses collaborative filtering (CF) and deep learning sequence models on the Musical Instrument Digital Interface (MIDI) content of songs to provide accurate recommendations, while also being able to generate a relevant, personalized explanation for each recommended song. Compared to state-of-the-art methods, our validation experiments showed that in addition to generating explainable recommendations, our model stood out among the top performers in terms of recommendation accuracy and the ability to handle the item cold start problem. Moreover, validation shows that our personalized explanations capture properties that are in accordance with the user’s preferences.

The Application of the Paradigm of Soft Set Logic Formula in Recommendation System

With the development of science and technology, information has shown explosive growth. It is difficult for information consumers to find useful information from a large amount of information. This article combines soft set theory and propositional calculus to propose a recommendation algorithm that meets the requirements of users. The algorithm describes user requirements in terms of soft set logic formulas and gives reasonable recommendation results by solving the normal form of the sub-formula and its realization in the corresponding soft subset. Numerical example shows that the algorithm can be successfully applied to the problem of song recommendation.

Expression X: Emotion Based Music Recommendation System

This Paper proposes method to recommend a set of songs based on the facial emotion state of the user. Emotion state of the user is detected with the help of google mobile vision SDK. The detected emotion state is fed to Expression-X algorithm that would sort the music (based on emotion value is keyed in) and generates a playlist which suites the emotion state of user. Since emotions are calculated based on the facial expression of a user, achieving 100% accuracy is undoubtedly hard as everyone has their own way of expressing emotions facially, with repetitive testing we have achieved 70–75% success rate in detecting the rite emotion state of the user, and generating the suitable set of song recommendation.

A Session-Based Song Recommendation Approach Involving User Characterization along the Play Power-Law Distribution

In recent years, streaming music platforms have become very popular mainly due to the huge number of songs these systems make available to users. This enormous availability means that recommendation mechanisms that help users to select the music they like need to be incorporated. However, developing reliable recommender systems in the music field involves dealing with many problems, some of which are generic and widely studied in the literature while others are specific to this application domain and are therefore less well-known. This work is focused on two important issues that have not received much attention: managing gray-sheep users and obtaining implicit ratings. The first one is usually addressed by resorting to content information that is often difficult to obtain. The other drawback is related to the sparsity problem that arises when there are obstacles to gather explicit ratings. In this work, the referred shortcomings are addressed by means of a recommendation approach based on the users’ streaming sessions. The method is aimed at managing the well-known power-law probability distribution representing the listening behavior of users. This proposal improves the recommendation reliability of collaborative filtering methods while reducing the complexity of the procedures used so far to deal with the gray-sheep problem.

MoodPlayer: An Emotion Based Music Player

This report describes, songs, as expressional medium, has always been a consistent choice to portray and comprehend the feelings of a human being. A trustworthy system of emotion base classification can go a long way in helping us parse their significance. However, there are no optimum outcomes in the research field in emotion base classification systems. In this paper, we present an optimum cross-platform music player, MoodPlayer, which will suggest the songs according to the current mood of user. MoodPlayer provides smart and accurate mood base song recommendation by integrating the competence of emotion context reasoning within our versatile music suggestion system. Our music player works in two modules: Emotion Module, Song and Recommendation Module. A photo of user’s face will be captured and taken as input in the initial steps of Emotion Module and using some algorithms the mood will be identified. The song and recommendation module proposes music to the end user by mapping their emotions to the mood type of the song and changes the playlist if the mood of the person is changed

Song Recommendations Based on Artists with Cosine Similarity Algorithms and K-Nearest Neighbor

The development of the music industry is currently growing rapidly, millions of music works continue to be issued by various music artists. As for the technologies also follows these developments, examples are mobile phones applications that have music subscription services, namely Spotify, Joox, GrooveShark, and others. Application-based services are increasingly in demand by users for streaming music, free or paid. In this paper, a music recommendation system is proposed, which the system itself can recommend songs based on the similarity of the artist that the user likes or has heard. This research uses Collaborative Filtering method with Cosine Similarity and K-Nearest Neighbor algorithm. From this research, a system that can recommend songs based on artists who are related to one another is generated.

Research on Recommendation Systems using Deep Learning Models

Recommender System is the effective tools that are accomplished of recommending the future preference of a set of products to the consumer and to predict the most likelihood items. Today, customers has the ability to purchase or sell different items with advancement of e-commerce website, nevertheless it made complicate to investigate the majority of appropriate items suitable for the interest of the consumer from many items. Due to this scenario, recommender systems that can recommend items appropriate for user's interest and likings have become mandatory. In recent days, various recommendation methods are applied to resolve the data abundance setback in numerous application areas like movie recommendation, e-commerce, news recommendation, song recommendation and social media. Even if all the available current recommender systems are successful in generating reasonable predictions, these recommendation system still facing the issues like accuracy, cold-start, sparsity and scalability problem. Deep learning, the recently developed sub domain of machine learning technique is utilized in recommendation systems to enhance the feature of predicted output. Deep Learning is used to generate recommendations and the research challenges specific to recommendation systems when using Deep Learning are also presented. In this research, the basic terminologies, the fundamental concepts of Recommendation engine and a wide-ranging review of deep learning models utilized in Recommender Systems are presented.

Heartbeats: music recommendation system with fuzzy inference engine

<p>In developing a music recommendation system, there are several factors that can contribute to the inefficiency in music selection. One of the problems persists during the music listening is that common music playing application lacks the ability to acquire context of the user. Another problem that common music recommendation system fails to address the is emotional impact of the recommended song. To address this gap, this paper presents a music recommendation system based on fuzzy inference engine that considers user activities and emotion as part of the recommendation parameters. The system includes building a smart music recommendation system that has user profiling capabilities to recommend correct songs based on the user’s preferences, mood and time. Findings of the this paper have shown that Heartbeats’s fuzzy inference engine has successfully achieved its aim, which is to improve users’ music listening experience by giving suitable song recommendation based on user context situation.</p>

Leveraging proficiency and preference for online Karaoke recommendation

Recently, many online Karaoke (KTV) platforms have been released, where music lovers sing songs on these platforms. In the meantime, the system automatically evaluates user proficiency according to their singing behavior. Recommending approximate songs to users can initialize singers’ participation and improve users’ loyalty to these platforms. However, this is not an easy task due to the unique characteristics of these platforms. First, since users may be not achieving high scores evaluated by the system on their favorite songs, how to balance user preferences with user proficiency on singing for song recommendation is still open. Second, the sparsity of the user-song interaction behavior may greatly impact the recommendation task. To solve the above two challenges, in this paper, we propose an informationfused song recommendationmodel by considering the unique characteristics of the singing data. Specifically, we first devise a pseudo-rating matrix by combing users’ singing behavior and the system evaluations, thus users’ preferences and proficiency are leveraged. Then wemitigate the data sparsity problem by fusing users’ and songs’ rich information in the matrix factorization process of the pseudo-ratingmatrix. Finally, extensive experimental results on a real-world dataset show the effectiveness of our proposed model.

Effects of Online Recommendations on Consumers’ Willingness to Pay

Recommender systems are an integral part of the online retail environment. Prior research has focused largely on computational approaches to improving recommendation accuracy, and only recently researchers have started to study their behavioral implications and potential side effects. We used three controlled experiments, in the context of purchasing digital songs, to explore the willingness-to-pay judgments of individual consumers after being shown personalized recommendations. In Study 1, we found strong evidence that randomly assigned song recommendations affected participants’ willingness to pay, even when controlling for participants’ preferences and demographics. In Study 2, participants viewed actual system-generated recommendations that were intentionally perturbed (introducing recommendation error), and we observed similar effects. In Study 3, we showed that the influence of personalized recommendations on willingness-to-pay judgments was obtained even when preference uncertainty was reduced through immediate and mandatory song sampling prior to pricing. The results demonstrate the existence of important economic side effects of personalized recommender systems and inform our understanding of how system recommendations can influence our everyday preference judgments. The findings have significant implications for the design and application of recommender systems as well as for online retail practices. The online appendix is available at https://doi.org/10.1287/isre.2017.0703 .

Multi-level Song Recommendation System based on SNS Posts and Sentiment Analysis

Multi-level Song Recommendation System based on SNS Posts and Sentiment Analysis

Multi-level Song Recommendation System based on SNS Posts and Sentiment Analysis

Multi-level Song Recommendation System based on SNS Posts and Sentiment Analysis

Efficient karaoke song recommendation via multiple kernel learning approximation

Online karaoke allows users to practice singing and distribute recordings. Different from traditional music recommendation, online karaoke need to consider users’ vocal competence besides their tastes. In this paper, we develop a karaoke recommender system by taking into account vocal competence. Alone this line, we propose a joint modeling method named MKLA by adopting bregman divergence as the regularizer in the formulation of multiple kernel learning. Specially, we first extract users’ vocal ratings from their singing recordings. Due to an ever-increasing number of recordings, the evaluations in large-scale kernel matrix may cost lots of time and internal storage. Therefore, we propose a sample compression method to eliminate users’ vocal ratings, exploit an MKL method, and learn the latent features of the vocal ratings. These latent features are simultaneously fed into a bregman divergence and then we use the trained classifier to predict the overall rating of a user with respect to a song. Enhanced by this new formulation, we develop the SMO method for optimizing the MKLA dual and present a theoretical analysis to show the lower bound of our method. With the estimated model, we compute the matching degree of users and songs in terms of pitch, volume and rhythm and recommend songs to users. Finally, we conduct extensive experiments with online karaoke data. The results demonstrate the effectiveness of our method.

Competence-Based Song Recommendation: Matching Songs to One’s Singing Skill

Singing is a popular social activity and a pleasant way of expressing one’s feelings. One important reason for unsuccessful singing performance is because the singer fails to choose a suitable song. In this paper, we propose a novel competence-based song recommendation framework for the purpose of singing. It is distinguished from most existing music recommendation systems which rely on the computation of listeners’ interests or similarity. We model a singer’s vocal competence as a singer profile, which takes voice pitch, intensity, and quality into consideration. Then we propose techniques to acquire singer profiles. We also present a song profile model which is used to construct a human annotated song database. Then we propose a learning-to-rank scheme for recommending songs by a singer profile. Finally, we introduce a reduced singer profile which can greatly simplify the vocal competence modelling process. The experimental study on real singers demonstrates the effectiveness of our approach and its advantages over two baseline methods.

Learning Content Similarity for Music Recommendation

Many tasks in music information retrieval, such as recommendation, and playlist generation for online radio, fall naturally into the query-by-example setting, wherein a user queries the system by providing a song, and the system responds with a list of relevant or similar song recommendations. Such applications ultimately depend on the notion of similarity between items to produce high-quality results. Current state-of-the-art systems employ collaborative filter methods to represent musical items, effectively comparing items in terms of their constituent users. While collaborative filter techniques perform well when historical data is available for each item, their reliance on historical data impedes performance on novel or unpopular items. To combat this problem, practitioners rely on content-based similarity, which naturally extends to novel items, but is typically outperformed by collaborative filter methods. In this paper, we propose a method for optimizing content-based similarity by learning from a sample of collaborative filter data. The optimized content-based similarity metric can then be applied to answer queries on novel and unpopular items, while still maintaining high recommendation accuracy. The proposed system yields accurate and efficient representations of audio content, and experimental results show significant improvements in accuracy over competing content-based recommendation techniques.

Repertoire Recommendations by Music Therapists for Geriatric Clients During Singing Activities

Little is known about the actual repertoire music therapists use with geriatric clients during singing activities, particularly whether the repertoire choices represent the young adult years of persons categorized as seniors, as previous researchers have suggested. Therefore, the purposes of this study were to: 1) determine specific repertoire music therapists are using with geriatric clients during singing activities in the music styles of popular, patriotic, hymn, folk, and musicals and 2) determine what decades their repertoire choices represented. Surveys were sent to 455 music therapists who worked with geriatric persons. Of these, 151 music therapists returned a survey, creating a 33% return rate. A combined total of songs (N = 1688) were recommended within the five music styles. Results found popular music was the style category with the greatest number of song recommendations followed by folk songs, songs from musicals, hymns, and patriotic songs respectively. A complete list of the repertoire is included.