Oct 2026· Proceedings of the Workshop on the ACM RecSys Challenge· 1 citation· 3 references
TL;DR
This diagnostic compares the full pipeline with reciprocal-rank fusion, ablate per-retriever features, and decompose ranking error into retrieval misses, reranking exclusions, and ranks-2–20 ordering loss and complement the leaderboard result with a diagnostic that fits on Train and evaluates the official Devset.
Abstract
Our submission under the team name niwatori ranked third overall in the RecSys Challenge 2026 Music-CRS task. The pipeline combines candidates from 14 retrieval sources while retaining, for each candidate, which sources returned it and their ranks and scores. It uses these signals together with context–track features in a LightGBM LambdaRank reranker. A Qwen3.6-27B responder verbalizes the top ranked tracks. The submission also ranked third in nDCG@20 on the final Blind-B leaderboard. We complement the leaderboard result with a diagnostic that fits on Train and evaluates the official Devset. In this diagnostic, we compare the full pipeline with reciprocal-rank fusion, ablate per-retriever features, and decompose ranking error into retrieval misses, reranking exclusions, and ranks-2–20 ordering loss. Code is available at https://github.com/ryowk/recsys2026-niwatori.
This paper describes the solution submitted by team Hallucinated to the ACM RecSys Challenge 2026, based on the TalkPlayData conversational music recommendation dataset. The Challenge poses a dual task: at every turn of a multi-turn dialogue, a system must both (i) rank the most relevant music tracks from the catalogue...
Abdallah Alkhetiar, Luigi Inguaggiato, Nicolò Locatelli et al.· Proceedings of the Workshop...· 1 citation
Team npatta01’s submission to the RecSys Challenge 2026 conversational music recommendation task is described and failure cases from the submitted run show extracted constraints the pipeline could not enforce.
Nidhin Pattaniyil, Semih Yagli, Tanwir Zaman· Proceedings of the Workshop...· 1 citation· ⚡1
We describe the PoliBaJukebox submission to the ACM RecSys Challenge 2026 on conversational music recommendation over the TalkPlayData 2 corpus. Our system implements a modular two-stage pipeline: a first stage retrieves candidates from the full track catalog using ten heterogeneous sources, fuses them with weighted Re...
Andrea Lops, Nicola Cipriani, Gabriele Colapinto et al.· Proceedings of the Workshop...· 1 citation
The ablation study shows that the contextual and utterance dense retrievers provide the largest gains in candidate coverage, while structural retrieval also improves the final ranking, and the benefit of combining complementary retrieval signals with a learned reranker for conversational music recommendation.
Youness Soussou, Loubna Mekouar, Youssef Iraqi· Proceedings of the Workshop...· 1 citation· ⚡1
The RecSys Challenge 2026 studies conversational music recommendation as a joint item recommendation and response generation problem: given a multi-turn dialogue, systems must retrieve relevant tracks from a large catalog and produce a grounded natural-language response. This paper presents the challenge task, dataset,...
Seungheon Doh, Sergio Oramas, B. Sguerra et al.· Proceedings of the Workshop...· 0 citations
The RecSys Challenge 2026 Music-CRS (TalkPlay) task formalizes this as two coupled sub-problems: given dialogue history and user context, retrieve a ranked list of the top-20 tracks from the full, unrestricted catalog, and generate a response that justifies the recommendation while sustaining conversational coherence.
Simran Sundrani, Mohan Bhambhani· Proceedings of the Workshop...· 1 citation· ⚡1
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