Music Recommendation Systems: Techniques, Use Cases, and Challenges

Schedl, Markus; Knees, Peter; McFee, Brian; Bogdanov, Dmitry

doi:10.1007/978-1-0716-2197-4_24

Cited by 26 publications

(3 citation statements)

References 102 publications

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“…These advantages justify making an investment in a robust architectural rec-ommendation system. However, several fundamental issues confront recommendation systems, including data sparsity, cold start challenges, lower accuracy, precision issues, information overload, and more [21], [22]. Implementing a single conventional algorithm to recommend products while accommodating evolving user behavior in the face of these challenges is quite demanding.…”

Section: A Recommendation Systemsmentioning

confidence: 99%

Exploring the Landscape of Hybrid Recommendation Systems in E-Commerce: A Systematic Literature Review

Bodduluri,

Palma,

Kurti

et al. 2024

IEEE Access

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This article presents a systematic literature review on hybrid recommendation systems (HRS) in the e-commerce sector, a field characterized by constant innovation and rapid growth. As the complexity and volume of digital data increases, recommendation systems have become essential in guiding customers to services or products that align with their interests. However, the effectiveness of singlearchitecture recommendation algorithms is often limited by issues such as data sparsity, challenges in understanding user needs, and the cold start problem. Hybridization, which combines multiple algorithms in different methods, has emerged as a dominant solution to these limitations. This approach is utilized in various domains, including e-commerce, where it significantly improves user experience and sales. To capture the recent trends and advancements in HRS within e-commerce over the past six years, we review the state-of-the-art overview of HRS within e-commerce. This review meticulously evaluates existing research, addressing primary inquiries and presenting findings that contribute to evidence-based decisionmaking, understanding research gaps, and maintaining transparency. The review begins by establishing fundamental concepts, followed by detailed methodologies, findings from addressing the research questions, and exploration of critical aspects of HRS. In summarizing and incorporating existing research, this paper offers valuable insights for researchers and outlines potential avenues for future research, ultimately providing a comprehensive overview of the current state and prospects of HRS in e-commerce.

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Section: A Recommendation Systemsmentioning

confidence: 99%

Exploring the Landscape of Hybrid Recommendation Systems in E-Commerce: A Systematic Literature Review

Bodduluri,

Palma,

Kurti

et al. 2024

IEEE Access

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show abstract

“…Recently, research on recommender systems emerged that aims at enhancing the traditional data-driven techniques with psychological constructs [27]. The users' personality traits were considered in [28], where the authors adapt the level of diversity in the recommendation list according to the personality traits of the user by reranking the results of a CF system.…”

Section: Related Workmentioning

confidence: 99%

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

Wang

Zhou

et al. 2022

Electronics

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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.

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“…For example, the music software Spotify suggests songs or albums you might want to listen to by categorising the music. They then categorise listeners to see if they would prefer to listen to Radiohead or Justin Bieber [6,7]. Reinforcement learning is something that does not require a domain expert, but requires constant progress towards a predetermined goal.…”

Section: Introductionmentioning

confidence: 99%

Application of supervised learning models in the Chinese futures market

Tang¹

2023

Preprint

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Based on the characteristics of the Chinese futures market, this paper builds a supervised learning model to predict the trend of futures prices and then designs a trading strategy based on the prediction results. The Precision, Recall and F1-score of the classification problem show that our model can meet the accuracy requirements for the classification of futures price movements in terms of test data. The backtest results show that our trading system has an upward trending return curve with low capital retracement.

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Music Recommendation Systems: Techniques, Use Cases, and Challenges

Cited by 26 publications

References 102 publications

Exploring the Landscape of Hybrid Recommendation Systems in E-Commerce: A Systematic Literature Review

Exploring the Landscape of Hybrid Recommendation Systems in E-Commerce: A Systematic Literature Review

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

Application of supervised learning models in the Chinese futures market

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