13 citations · 27 across the 10 of their papers we have counts for
12 papers
Overview and Analysis of the RecSys Challenge 2026: Conversational Music Recommendation
Seungheon Doh, Sergio Oramas, Bruno Sguerra +3
The RecSys Challenge 2026 studies conversational music recommendation as a joint item recommendation and response generation problem: given a multi-turn dialogue, systems must retr…
LLM-as-a-Judge for Evaluating System Responses in Conversational Music Recommendation
Seungheon Doh, Bruno Sguerra, Sergio Oramas +2
Conversational Recommendation Systems (CRS) aim to achieve two primary objectives: recommending relevant items and generating natural language responses. While recommendation accur…
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…
"Beyond the past": Leveraging Audio and Human Memory for Sequential Music Recommendation
Viet-Anh Tran, Bruno Sguerra, Gabriel Meseguer-Brocal +2
On music streaming services, listening sessions are often composed of a balance of familiar and new tracks. Recently, sequential recommender systems have adopted cognitive-informed…
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.…
Modeling Musical Genre Trajectories through Pathlet Learning
Lilian Marey, Charlotte Laclau, Bruno Sguerra +2
The increasing availability of user data on music streaming platforms opens up new possibilities for analyzing music consumption. However, understanding the evolution of user prefe…
Uncertainty in Repeated Implicit Feedback as a Measure of Reliability
Bruno Sguerra, Viet-Anh Tran, Romain Hennequin +1
Recommender systems rely heavily on user feedback to learn effective user and item representations. Despite their widespread adoption, limited attention has been given to the uncer…
Transformers Meet ACT-R: Repeat-Aware and Sequential Listening Session Recommendation
Viet-Anh Tran, Guillaume Salha-Galvan, Bruno Sguerra +1
Music streaming services often leverage sequential recommender systems to predict the best music to showcase to users based on past sequences of listening sessions. Nonetheless, mo…
Modeling Activity-Driven Music Listening with PACE
Lilian Marey, Bruno Sguerra, Manuel Moussallam
While the topic of listening context is widely studied in the literature of music recommender systems, the integration of regular user behavior is often omitted. In this paper, we…
Ex2Vec: Characterizing Users and Items from the Mere Exposure Effect
Bruno Sguerra, Viet-Anh Tran, Romain Hennequin
The traditional recommendation framework seeks to connect user and content, by finding the best match possible based on users past interaction. However, a good content recommendati…
Attention Mixtures for Time-Aware Sequential Recommendation
Viet-Anh Tran, Guillaume Salha-Galvan, Bruno Sguerra +1
Transformers emerged as powerful methods for sequential recommendation. However, existing architectures often overlook the complex dependencies between user preferences and the tem…
Discovery Dynamics: Leveraging Repeated Exposure for User and Music Characterization
Bruno Sguerra, Viet-Anh Tran, Romain Hennequin
Repetition in music consumption is a common phenomenon. It is notably more frequent when compared to the consumption of other media, such as books and movies. In this paper, we sho…