Recommendation of Songs in Music Streaming Services: Dealing with Sparsity and Gray Sheep Problems

Sánchez-Moreno, Diego; González, Ana Belén Gil; Vicente, María Dolores Muñoz; Batista, Vivian F. López

doi:10.1007/978-3-319-61578-3_21

Cited by 7 publications

(2 citation statements)

References 11 publications

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“…Since this information is collected by the streaming systems in an easy and regular way, some drawbacks regarding the need to acquire additional data, as explicit ratings, music metadata, or audio features, are avoided. e work is the continuation of a previous proposal for artist recommendation [50] and another preliminary study [51], which has been extended and adapted for recommending songs. e improvement of results compared to the main CF methods is achieved by focusing on two major aspects: a new way of obtaining implicit ratings from user sessions and the characterization of users according to the place of the songs played by them in the power-law distribution of play frequency.…”

Section: Improving Cf Approaches Formentioning

confidence: 90%

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

et al. 2020

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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.

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Section: Improving Cf Approaches Formentioning

confidence: 90%

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

et al. 2020

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“…In the field of music information retrieval (MIR), research on music streaming services includes studies on improving recommendation algorithms [9][10][11], understanding user behavior and patterns of use [12][13][14][15][16], and studying user experiences and interfaces [17][18][19][20][21]. These studies aimed to enhance overall user satisfaction and engagement with music streaming services by providing personalized recommendations, improving the user interface, and identify-ing the factors that influenced user behaviors and preferences.…”

Section: Related Workmentioning

confidence: 99%

History of Curriculums of the Department of Korean Traditional Music in Seoul National University

Choi¹,

강사²

2019

Tongyang Ŭmak

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As sleep disorders are becoming more prevalent there is an urgent need to classify sleep stages in a less disturbing way. In particular, sleep-stage classification using simple sensors, such as single-channel electroencephalography (EEG), electrooculography (EOG), electromyography (EMG), or electrocardiography (ECG) has gained substantial interest. In this study, we proposed a sleep model that predicts the next sleep stage and used it to improve sleep classification accuracy. The sleep models were built using sleep-sequence data and employed either statistical n-gram or deep neural network-based models. We developed beam-search decoding to combine the information from the sensor and the sleep models. Furthermore, we evaluated the performance of the n-gram and long short-term memory (LSTM) recurrent neural network (RNN)-based sleep models and demonstrated the improvement of sleep-stage classification using an EOG sensor. The developed sleep models significantly improved the accuracy of sleep-stage classification, particularly in the absence of an EEG sensor.

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David – A Novel Approach to an Industrial Recommender System

Marques¹,

Martins²,

García

2022

Highlights in Practical Applications of Agents, Multi-Agent Systems, and Complex Systems Simulation. The PAAMS Collection

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Recommendation of Songs in Music Streaming Services: Dealing with Sparsity and Gray Sheep Problems

Cited by 7 publications

References 11 publications

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

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

History of Curriculums of the Department of Korean Traditional Music in Seoul National University

David – A Novel Approach to an Industrial Recommender System

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