Just Ask for Music (JAM): Multimodal and Personalized Natural Language Music Recommendation
Proceedings of the Nineteenth ACM Conference on Recommender Systems
PostDoc
Alessandro B. Melchiorre, PhD
Johannes Kepler University Linz
About
I am a PostDoc in Machine Learning and Computer Science at the Institute of Computational Perception at Johannes Kepler University Linz, Austria.
My current research is on Generative Retrieval applied to Recommendation and Multimodal Recommender Systems.
My past research specializes in recommender system algorithms, explainable AI, and fairness in machine learning. My work has been published in esteemed conferences and journals such as RecSys, ECML-PKDD, ECIR, AI Magazine, and Information Processing & Management.
Additionally, I led two science communication projects showcased at the international Ars Electronica Festival in Linz,
Research output
Publications
Mitigating Gender Bias in Job Ranking Systems Using Job Advertisement Neutrality
Proceedings of the Fourth Workshop on NLP for Positive Impact (NLP4PI)
Modular Debiasing of Latent User Representations in Prototype-based Recommender Systems
Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2024)
Award · Best Student Paper Award
Emotion-aware Music Tower Blocks (EmoMTB): An Intelligent Audiovisual Interface for Music Discovery and Recommendation
International Journal of Multimedia Information Retrieval (IJMIR)
EmoMTB: Emotion-aware Music Tower Blocks
Proceedings of the 2022 ACM International Conference on Multimedia Retrieval (ICMR)
Explainability in music recommender systems
AI Magazine
Exploring Cross-group Discrepancies in Calibrated Popularity for Accuracy/Fairness Trade-off Optimization
Proceedings of the 2nd Workshop on Multi-Objective Recommender Systems co-located with 16th ACM Conference on Recommender Systems (RecSys 2022), Seattle, WA, USA, 18th-23rd September
ProtoMF: Prototype-based Matrix Factorization for Effective and Explainable Recommendations
Proceedings of the 16th ACM Conference on Recommender Systems (RecSys)
Analyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected?
Proceedings of the 15th ACM Conference on Recommender Systems (RecSys)
Investigating Gender Fairness of Recommendation Algorithms in the Music Domain
Information Processing & Management
LEMONS: Listenable Explanations for Music recOmmeNder Systems
Proceedings of the 43rd European Conference on Information Retrieval (ECIR)
Personality Bias of Music Recommendation Algorithms
Proceedings of the 14th ACM Conference on Recommender Systems (RecSys)
Personality Correlates of Music Audio Preferences for Modelling Music Listeners
Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization (UMAP)