Showdown 2024: Relevance Feedback Meets Conversational Search

CompCQR: Compositional Query Generation for Training-Free Conversational Search

Multi-turn interactions with LLMs are becoming increasingly common in information-seeking scenarios. However, user queries are often ambiguous and context-dependent, making them ill-suited for direct use as retriever queries. Conversational query reformulation (CQR) addresses this issue by rewriting the current utterance into a stand-alone query grounded in the dialogue history. Recent LLM-based CQR approaches achieve strong performance; however, their repeated LLM invocations and misalignment with downstream retrievers remain challenges. In this work, we begin from the observation that retrievers are highly sensitive to content ordering: simply reordering the same content can lead to changes in retrieval coverage and performance. Based on this, we propose a novel training-free method that generates a very large number of queries with minimal LLM usage by compositionally combining a small set of atomic components. We further apply LLM reasoning to construct a high-quality document set that balances precision and recall while capturing the user's core intent. Our framework generalizes across both open- and closed-source LLMs as well as dense and sparse retrievers. It achieves strong performance on four widely used conversational benchmarks, with up to 22.5% relative MRR improvement over the previous state-of-the-art baseline with far fewer LLM calls.

Lab Overview

Conversational Query Reformulation Using Fine-Grained Retrieval and Keyword Augmentation

Conversational Query Reformulation (CQR) is an important component in Conversational Question Answering (ConvQA), where user queries are often incomplete, ambiguous, and dependent on previous dialogue turns. Recent CQR approaches have shown the effectiveness of large language models (LLMs) in generating standalone queries. However, rewritten queries do not always lead to optimal retrieval performance. This paper presents a retrieval-aware CQR framework that integrates fine-grained retrieval feedback and keyword augmentation to improve downstream document retrieval. Instead of treating the LLM-rewritten query as the final search query, our framework uses it to retrieve candidate documents, identify relevant paragraph-level evidence, generate a pseudo-answer, and extract semantically relevant keywords. These keywords are then filtered based on their alignment with the reformulated query and dialogue history before being incorporated into the final query. We evaluate the proposed framework on a sampled subset of the QReCC dataset using BM25 and GTR as sparse and dense retrieval backbones. The experimental results show that our proposed approach improves retrieval accuracy compared to the baseline model across all metrics such as Recall@10, MRR and NDCG. Specifically, under the BM25 sparse retrieval framework, our best-performing configuration achieves improvements in Recall@10 (81.29), MRR (56.06) and NDCG (60.92) compared to the baseline. A comparable pattern of substantial gains holds under the GTR dense retrieval framework, achieving a Recall@10 (76.51), MRR (53.26) and NDCG (57.54), indicating the effectiveness of our method across different retrieval paradigms.

Questioning Matters: A Controlled Study of Question Generation in Conversational Image Retrieval

Conversational Image Retrieval (CIR) refines image search through multi-turn interaction, where the Questioner plays a central role in eliciting information about the user’s target. However, existing CIR systems are commonly evaluated in end-to-end settings, making it difficult to determine whether performance gains originate from questioning strategies, retrieval backbones, answering behavior, or interaction protocols. We introduce a controlled benchmark for studying questioning strategies in CIR. The proposed benchmark isolates the Questioner module by keeping the retriever, answerer, dialogue budget, decoding setting, and evaluation pipeline fixed across all methods. Under this unified protocol, we compare representative strategies including Blind, Text-based, Text-based Recon, Top-K Guided, and Hybrid. Experimental analysis reveals a clear stage-dependent trade-off between efficiency and discriminative capability. Text-based strategies remain computationally lightweight but become increasingly vulnerable to contextual drift over extended interaction, whereas Top-K Guided improves retrieval refinement at the cost of higher inference latency. Overall, the proposed benchmark provides a reproducible framework for analyzing conversational questioning behavior in CIR systems.

Overview and Analysis of the RecSys Challenge 2026: Conversational Music Recommendation

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, evaluation protocol, and official results. Beyond the leaderboard, we analyze the 16 accepted systems through a common retrieve–rerank–generate framework and examine how recommendation performance varies across users, requests, and dialogue contexts. Strong systems commonly combine heterogeneous candidate sources and preserve source-specific evidence for learned reranking. Across the system papers and our organizer-side analysis, robust design also means 1) grounding cold-start retrieval in multi-turn conversation and item signals, 2) using intent detectors, and 3) modeling the full multi-turn context rather than the current query alone. We further identify limitations of the benchmark and evaluation protocol, including single-ground-truth relevance and teacher-forced evaluation of synthetic dialogues. Together, these findings provide practical guidance for future conversational recommender systems and shared evaluation efforts.

MiniMaestro: Resource-Conscious Conversational Music Recommendation with a Single Open-Weight 8B Model

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.