An Analysis of Global and RegionalMainstreaminess for Personalized MusicRecommender Systems

Schedl, Markus; Bauer, Christine

doi:10.13052/jmm1550-4646.1415

Cited by 9 publications

(3 citation statements)

References 31 publications

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“…e traditional content-based recommendation algorithm first gets the items that have interacted with users and then gets the user's preference model through similar user's active behaviors such as users' likes or ratings of items. User feedback on the project includes implicit and explicit ones [24,25]. e former is the log of users' marks when using the system, which reflects users' interest in the project, such as browsing the lyrics of songs and the number of times the songs are played repeatedly.…”

Section: Application Of Deep Learning In Recommendation Systemmentioning

confidence: 99%

Research on Music Content Recognition and Recommendation Technology Based on Deep Learning

Yang

2022

Security and Communication Networks

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With the development of information technology, various cloud music services are gradually emerging, which has fully changed and enriched people’s music life. How to propose the songs that consumers anticipate from the enormous song data is one of the key goals of the music recommendation system. This research aims to create a better music algorithm that incorporates user data for deep learning, a candidate matrix compression technique for suggestion improvement, accuracy, recall rate, and other metrics as evaluation criteria. In terms of recommendation methods, the music-music recommendation method based on predicting user behavior data and the recommendation method based on automatic tag generation are proposed. The music features obtained by audio processing are fully utilized, and the depth content information in music audio data is combined with other data for recommendation, which improves the tag quality and avoids the problem of low coverage. The results show that this model can extract the effective feature representation of songs in different classification criteria and achieve a good classification effect simultaneously.

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Section: Application Of Deep Learning In Recommendation Systemmentioning

confidence: 99%

Research on Music Content Recognition and Recommendation Technology Based on Deep Learning

Yang

2022

Security and Communication Networks

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“…In this chapter we focus on the bias towards the so-called mainstream users. A mainstream user often prefers items liked by many people and also reacts negatively to items widely disliked by others [106]. Contrary to this, non-mainstream users typically show interest on rarely-visited items or have an opposite attitude towards widely accepted or rejected items.…”

Section: Introductionmentioning

confidence: 95%

“…They observed that, although only taking a small proportion of users, kids and adolescents have significantly different preferences from other age groups in terms of music genres, and the recommendation performance on these two groups is also distinctive among all users. To repair the unfairness caused by the mainstream bias, several recent works aim at identifying non-mainstream music listeners and using the power of cultural aspects [106,89] and human memories [61] to better profile these underrepresented users in recommender systems. Despite the reported progress, existing methods to alleviate the mainstream bias usually rely on their specific definitions of mainstreamness, which may limit the findings.…”

Section: Biases In Recommender Systemsmentioning

confidence: 99%

Metric Optimization and Mainstream Bias Mitigation in Recommender Systems

2023

SIGIR Forum

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Recommender Systems have drawn extensive attention in recent decades. The most important factor to make a recommender succeed is user satisfaction, which is largely reflected by the recommendation accuracy. In accordance, this thesis dives into recommendation accuracy from two different perspectives that, unfortunately, are at tension with each other: achieving the maximum overall recommendation accuracy, and balancing that accuracy among all users. The first part of this thesis focuses on maximizing the overall recommendation accuracy Li et al. [2021a]. This accuracy is usually evaluated with some user-oriented metrics tailored to the recommendation scenario. However, recommendation models could be trained to maximize some other generic criteria that do not necessarily align with the criteria ultimately captured by the metric above. Recent research usually assumes that the metric used for evaluation is also the metric used for training. We challenge this assumption, mainly because some metrics are more informative than others. Indeed, we show that models trained via the optimization of a loss inspired by Rank-Biased Precision (RBP) tend to yield higher accuracy, even when accuracy is measured with metrics other than RBP. However, the superiority of this RBP-inspired loss stems from further benefiting users who are already well-served, rather than helping those who are not. This observation inspires the second part of this thesis, where our focus turns to helping nonmainstream users, who are difficult to recommend to either because of the lack of data for effective modeling or a niche taste that is hard to match similar users. Our first effort consists in using side data, beyond the user-item interaction matrix, so that users and items are better represented. This will be of benefit especially for the non-mainstream users, for which the user-item matrix alone is ineffective. We propose Neural AutoEncoder Collaborative Filtering (NAECF) Li et al. [2021b], an adversarial learning architecture that, in addition to maximizing the recommendation accuracy, leverages side data to preserve the intrinsic properties of users and items and show that NAECF leads to better recommendations specially for non-mainstream users, while at the same time there is a marginal loss for the mainstream ones. Our second effort Li et al. [2023] consists in explicitly focusing more on non-mainstream users in a recommendation model. In particular, we propose a mechanism based on cost-sensitive learning that weighs users according to their mainstreamness, so that they get more attention during training. The result is a recommendation model tailored to non-mainstream users, that narrows the accuracy gap, and again at negligible cost to the mainstream users. Awarded by : Delft University of Technology, Delft, The Netherlands on 14 November 2023. Supervised by : Alan Hanjalic and Julián Urbano. Available at : https://doi.org/10.4233/uuid:235a5f5c-5e18-4e67-a8e6-84a7e310db12.

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Research on Music Database Construction Combining Big Data Analysis and Machine Learning Algorithm

Yao,

2023

Learning and Analytics in Intelligent Systems

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An Analysis of Global and RegionalMainstreaminess for Personalized MusicRecommender Systems

Cited by 9 publications

References 31 publications

Research on Music Content Recognition and Recommendation Technology Based on Deep Learning

Research on Music Content Recognition and Recommendation Technology Based on Deep Learning

Metric Optimization and Mainstream Bias Mitigation in Recommender Systems

Research on Music Database Construction Combining Big Data Analysis and Machine Learning Algorithm

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