Merge pull request #63 from deezer/RECSYS25-REACTA

Tran RecSys25-Reacta

Author: 0xpi

_posts/2025-09-07-recsys-tran.md

@@ -0,0 +1,34 @@
+---
+layout: post
+title: "'Beyond the past': Leveraging Audio and Human Memory for Sequential Music Recommendation"
+date: 2025-10-06 16:00:00 +0200
+category: Publication
+author: vatran
+readtime: 5
+people:
+ - vatran
+ - bmassonisguerra
+ - gmeseguerbrocal
+ - lbriand
+ - manuel.moussallam
+publication_type: conference
+publication_title: "'Beyond the past': Leveraging Audio and Human Memory for Sequential Music Recommendation"
+publication_year: 2025
+publication_authors: Viet-Anh Tran, Bruno Sguerra, Gabriel Meseguer-Brocal, Lea Briand, Manuel Moussallam
+publication_conference: RecSys
+publication_preprint: "https://arxiv.org/pdf/2507.17356"
+publication_code: "https://github.com/deezer/recsys25-reacta"
+domains: 
+ - RECSYS
+---
+
+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 approaches, such as Adaptive Control of Thought-Rational (ACT-R), to successfully improve the prediction of the most relevant tracks for the next user session. However, one limitation of using a model inspired by human memory (or the past), is that it struggles to recommend new tracks that users have not previously listened to. To bridge this gap, here we propose a model that leverages audio information to predict in advance the ACT-R-like activation of new tracks and incorporates them into the recommendation scoring process. We demonstrate the empirical effectiveness of the proposed model using proprietary data, which we publicly release along with the model's source code to foster future research in this field.
+
+<div class="publication-illustration">
+    <img
+        style="width: 75%;"
+        src="{{ '/static/images/publis/tran25recsys/reacta.png' | prepend: site.url }}"
+        alt="Architecture of REACTA model (dashed arrows are for inference time)."/>
+</div>
+
+This paper has been accepted for publication in the proceedings of the 19th ACM Conference on Recommender Systems (RecSys 2025).