Diversity by Design in Music Recommender Systems

Porcaro, Lorenzo; Castillo, Carlos

doi:10.5334/tismir.106

Cited by 5 publications

(4 citation statements)

References 69 publications

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“…As shown in equation (10) and Figure 7 , the error rate has been deliberated based on the data set [27]. In this case, S stands for the collection of ratings.…”

Section: Error Rate (%)mentioning

confidence: 99%

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Western Music History Recommendation System Based on Internet-of-Things Data Analysis Technology

Qiu

Jia

2022

Mobile Information Systems

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The quantity of Western music works is growing at an ever-increasing rate, making it difficult for music listeners to identify their favorites quickly. Thus, the music recommendation algorithm targeted music works based on previous user actions, reducing user weariness, and improving overall user experiences. This can minimize the exhaustion experienced by the consumers and increase the overall user experience. In this paper, the Internet of Things-based Western Music Recommendation (IoT-WMR) system can provide music listeners with reliable recommendations. It is used to categorize Western music genres such as traditional music, rock, jazz, and Hip–Hop/Rap. Two distinct activation functions and two different gradient descent techniques are compared and contrasted using the convolutional neural network (CNN). The two classification techniques can be compared depending on the spectrum and feature frequency of the spectrum and musical notes. This paper provides a music classification algorithm that can augment classification approaches in evaluating musical data.

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“…As shown in equation (10) and Figure 7 , the error rate has been deliberated based on the data set [27]. In this case, S stands for the collection of ratings.…”

Section: Error Rate (%)mentioning

confidence: 99%

“…e Internet of ings (IoT) has recovered multimodal information, including audio, text, video, and music, with the rapid growth of the Internet in recent decades [9,10]. Music services, for example, allow users to stream their favorite tunes at any time and from any location over the Internet [11].…”

Section: Introductionmentioning

confidence: 99%

Western Music History Recommendation System Based on Internet-of-Things Data Analysis Technology

Qiu

Jia

2022

Mobile Information Systems

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“…Analyzing the relationship between listeners and Music RS, diversity may be traced back on the one side to the features of the recommended items (e.g. tracks, playlists, or artists), on the other side to the characteristics of the users exposed to such items, and moreover to the interaction between users and items [20]. Whilst the latter aspect has been the focus of most of the computational approaches proposed in the Music RS literature, in this work we center our attention on the concept of perceived diversity, intended as the diversity of the items as perceived by the users.…”

Section: Introductionmentioning

confidence: 99%

“…Scholars in fields such as Information Retrieval (IR) and Human-Computer Interaction (HCI) continue to question how diversity can be operationally defined [1], how it is perceived by information systems' users [23], and what are the implications to be considered when designing diversity metrics [16]. Such foundational research is still missing in the MIR and Music RS literature, which instead has mostly focused on empirical analysis [20].…”

Section: Introductionmentioning

confidence: 99%

Diversity in the Music Listening Experience: Insights from Focus Group Interviews

Porcaro¹,

Castillo²

2022

Preprint

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Music listening in today's digital spaces is highly characterized by the availability of huge music catalogues, accessible by people all over the world. In this scenario, recommender systems are designed to guide listeners in finding tracks and artists that best fit their requests, having therefore the power to influence the diversity of the music they listen to. Albeit several works have proposed new techniques for developing diversity-aware recommendations, little is known about how people perceive diversity while interacting with music recommendations. In this study, we interview several listeners about the role that diversity plays in their listening experience, trying to get a better understanding of how they interact with music recommendations. We recruit the listeners among the participants of a previous quantitative study, where they were confronted with the notion of diversity when asked to identify, from a series of electronic music lists, the most diverse ones according to their beliefs. As a follow-up, in this qualitative study we carry out semi-structured interviews to understand how listeners may assess the diversity of a music list and to investigate their experiences with music recommendation diversity. We report here our main findings on 1) what can influence the diversity assessment of tracks and artists' music lists, and 2) which factors can characterize listeners' interaction with music recommendation diversity.

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Diversity in the Music Listening Experience: Insights from Focus Group Interviews

Porcaro

Castillo

2022

ACM SIGIR Conference on Human Information Interaction and Retrieval

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Diversity by Design in Music Recommender Systems

Cited by 5 publications

References 69 publications

Western Music History Recommendation System Based on Internet-of-Things Data Analysis Technology

Western Music History Recommendation System Based on Internet-of-Things Data Analysis Technology

Diversity in the Music Listening Experience: Insights from Focus Group Interviews

Diversity in the Music Listening Experience: Insights from Focus Group Interviews

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