Content-driven music recommendation: Evolution, state of the art, and challenges
The music domain is among the most important ones for adopting recommender systems
technology. In contrast to most other recommendation domains, which predominantly rely on …
technology. In contrast to most other recommendation domains, which predominantly rely on …
Integrating the act-r framework with collaborative filtering for explainable sequential music recommendation
M Moscati, C Wallmann, M Reiter-Haas… - Proceedings of the 17th …, 2023 - dl.acm.org
Music listening sessions often consist of sequences including repeating tracks. Modeling
such relistening behavior with models of human memory has been proven effective in …
such relistening behavior with models of human memory has been proven effective in …
Harmonizing minds and machines: survey on transformative power of machine learning in music
J Liang - Frontiers in Neurorobotics, 2023 - frontiersin.org
This survey explores the symbiotic relationship between Machine Learning (ML) and music,
focusing on the transformative role of Artificial Intelligence (AI) in the musical sphere …
focusing on the transformative role of Artificial Intelligence (AI) in the musical sphere …
Protomf: Prototype-based matrix factorization for effective and explainable recommendations
Recent studies show the benefits of reformulating common machine learning models
through the concept of prototypes–representatives of the underlying data, used to calculate …
through the concept of prototypes–representatives of the underlying data, used to calculate …
Flow moods: Recommending music by moods on deezer
T Bontempelli, B Chapus, F Rigaud, M Morlon… - Proceedings of the 16th …, 2022 - dl.acm.org
The music streaming service Deezer extensively relies on its Flow algorithm, which
generates personalized radio-style playlists of songs, to help users discover musical …
generates personalized radio-style playlists of songs, to help users discover musical …
Recommender systems: Trends and frontiers
Recommender systems (RSs), as used by Netflix, YouTube, or Amazon, are one of the most
compelling success stories of AI. Enduring research activity in this area has led to a …
compelling success stories of AI. Enduring research activity in this area has led to a …
Of spiky SVDs and music recommendation
The truncated singular value decomposition is a widely used methodology in music
recommendation for direct similar-item retrieval and downstream tasks embedding musical …
recommendation for direct similar-item retrieval and downstream tasks embedding musical …
Justification vs. Transparency: Why and How Visual Explanations in a Scientific Literature Recommender System
Significant attention has been paid to enhancing recommender systems (RS) with
explanation facilities to help users make informed decisions and increase trust in and …
explanation facilities to help users make informed decisions and increase trust in and …
Investigating the robustness of sequential recommender systems against training data perturbations: an empirical study
Sequential Recommender Systems (SRSs) have been widely used to model user behavior
over time, but their robustness in the face of perturbations to training data is a critical issue …
over time, but their robustness in the face of perturbations to training data is a critical issue …
Soft Contrastive Sequential Recommendation
Contrastive learning has recently emerged as an effective strategy for improving the
performance of sequential recommendation. However, traditional models commonly …
performance of sequential recommendation. However, traditional models commonly …