From the 1 of 23 linked papers with an AI index.
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Adaptive Autoguidance for Item-Side Fairness in Diffusion Recommender Systems
Zihan Li, Gustavo Escobedo, Marta Moscati +2
Diffusion recommender systems achieve strong recommendation accuracy but often suffer from popularity bias, resulting in unequal item exposure. To address this shortcoming, we intr…
Music Recommendation with Large Language Models: Challenges, Opportunities, and Evaluation
Elena V. Epure, Yashar Deldjoo, Bruno Sguerra +2
Music Recommender Systems (MRSs) have long relied on an information retrieval framing, where progress is measured mainly through accuracy on retrieval-oriented subtasks. While effe…
Single-Branch Network Architectures to Close the Modality Gap in Multimodal Recommendation
Christian Ganhör, Marta Moscati, Anna Hausberger +2
Traditional recommender systems rely on collaborative filtering, using past user-item interactions to help users discover new items in a vast collection. In cold start, i.e., when…
Parameter-Efficient Single Collaborative Branch for Recommendation
Marta Moscati, Shah Nawaz, Markus Schedl
Recommender Systems (RS) often rely on representations of users and items in a joint embedding space and on a similarity metric to compute relevance scores. In modern RS, the modul…
Just Ask for Music (JAM): Multimodal and Personalized Natural Language Music Recommendation
Alessandro B. Melchiorre, Elena V. Epure, Shahed Masoudian +4
Natural language interfaces offer a compelling approach for music recommendation, enabling users to express complex preferences conversationally. While Large Language Models (LLMs)…
Familiarizing with Music: Discovery Patterns for Different Music Discovery Needs
Marta Moscati, Darius Afchar, Markus Schedl +1
Humans have the tendency to discover and explore. This natural tendency is reflected in data from streaming platforms as the amount of previously unknown content accessed by users.…