A Personalized Music Recommender System Using User Contents, Music Contents and Preference Ratings

Su, Ja-Hwung; Chin, Chu-Yu; Liao, Yi-Wen; Yang, Hsiao-Chuan; Tseng, Vincent S.; Hsieh, Sun-Yuan

doi:10.1142/s2196888820500049

Cited by 7 publications

(4 citation statements)

References 11 publications

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“…Domestic scholars' research on music individualization proposal has also formed a series of scientific research results [ 19 ]. Su et al used spectrogram analysis to obtain the characteristic data matrix of musical works and calculated the similarity between stored music and query data to achieve Top-N proposal [ 20 ]. Zhang et al studied the multi-domain recommender systems, because traditional recommender systems only consider items in the same field, while cross-field recommender systems support operations on items in different fields, making the recommender system more accurate [ 21 ].…”

Section: Related Workmentioning

confidence: 99%

Music Individualization Recommendation System Based on Big Data Analysis

Pengfei

2022

Computational Intelligence and Neuroscience

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This study discovers a certain complementary relationship between different algorithms after conducting a comprehensive and in-depth analysis of proposal algorithms. This study proposes a big data music individualization proposal method based on big data analysis, which integrates user behaviour, behaviour context, user information, and music work information, based on traditional music proposal methods; improves the collaborative filtering proposal algorithm based on user behaviour; and calculates the semantic similarity between lyrics, as well as the song co-occurrence similarity based on the user’s music download history. Because the lyrics represent the thoughts and feelings that the song wishes to convey to the listeners, the proposal module is completed, and the music proposal system is realized, by combining the two different similar information, using the improved algorithm and the Hadoop distributed framework. The music similarity and label similarity are combined to alleviate the problem of cold start and data sparseness, and a mixed similarity calculation formula is proposed to calculate the similarity between music. The accuracy similarity of the big data music proposal model proposed in this study is improved by about 20% through experimental comparison when compared with the collaborative filtering model and the hybrid model. It reflects the efficiency, scalability, and stability of the music proposal system as well as the ability to meet users’ individual music needs.

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Section: Related Workmentioning

confidence: 99%

Music Individualization Recommendation System Based on Big Data Analysis

Pengfei

2022

Computational Intelligence and Neuroscience

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“…AV shown in Equation ( 1) and AR shown in Equation (2). We also compared recommendations delivered by IE Inclinator using PP with the recommendations obtained using a DA only shown in Equation (8) to show the net impact of the DA and DR on the quality of recommendations generated using PP shown in Equation (10). We computed MAE and RMSE for the output delivered using each of these four methods on both datasets to prove the effectiveness of the proposed PP used by IE inclinator and show the plots in Figures 9 and 10 respectively.…”

Section: 3effectiveness Of Pragmatic Propensity Used By Ie Inclinatormentioning

confidence: 99%

Entropy for item inclination in sub-community based recommender system

2022

IJATEE

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Machine learning (ML) entails self-learning via data usage and experience [1]. It requires no human intervention to uncover patterns in data [2]. Recent work in this area includes classification of liver tumours [3]; extraction of clinical attributes from breast cancer dataset [4]; prediction of student performance [5]; accurate recognition of complex physical human activity acquired using body-worn sensors [6]. Recommender system (RS) is also an exciting application of ML for suggesting relevant items to a user [7]. ML driven recommendation engines [8] have become ubiquitous in the last few decades. Intelligent web engines have crept in everywhere, recommending everything from movies, songs, food, social media posts to anything conceivable. Unconsciously, everybody is following these recommendations. The apparent reasons are convenience and satisfaction; else, dealing with profusion of information on the web is quite cumbersome. *Author for correspondence Famous online service providers like Facebook, Netflix, Spotify, Amazon, and LinkedIn use recommendation engines to boost sales and enhance customer satisfaction by utilising data filtering techniques of underlying RS [9]. RS uses traditional filtering techniques [10, 11] viz. collaborative filtering (CF), content-based filtering (CbF), demographic filtering (DF), and knowledgebased filtering (KF), along with hybrid filtering techniques that combine benefits of former techniques [12]. Liao et al. have found that users trust systems that use CF for recommendation over those using CbF or DF and have given pointers for solving cold-start problems [13]. CF is the most sought-after technique that collects users" preferences and predicts their interests. However, unfortunately it suffers from cold-start problems (non-availability of preference information for new user/item). Recent research points towards the inclusion of user demographic attributes (age, gender and location) to abate these problems [14]. Recently, González et al. have also utilized demographic information to evaluate the bias and unfairness of recommendations given to the minority groups [15].

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“…Music recommendation system usually consists of three aspects: user preference information, music description information and recommendation algorithm [7]. Recommendation algorithms, as the key technology of music recommendation system, generally include collaborative filtering algorithms, content-based recommendation algorithms and hybrid recommendation algorithms according to the specificity of the music recommendation problem [8]. Music recommendation algorithms will not only rely on data such as user behavior, preferences and ratings, but also take into account the characterization of items that users like [9].…”

Section: Introductionmentioning

confidence: 99%

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

He,

2024

ICST Transactions on Scalable Information Systems

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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.

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A Personalized Music Recommender System Using User Contents, Music Contents and Preference Ratings

Cited by 7 publications

References 11 publications

Music Individualization Recommendation System Based on Big Data Analysis

Music Individualization Recommendation System Based on Big Data Analysis

Entropy for item inclination in sub-community based recommender system

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

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