TL;DR
This work extends the existing Exquisitor URF system with conversational search, and the user interface has been completely redesigned to prepare for future integration with conversational search and better support novice users.
This work proposes 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 and applies LLM reasoning to construct a high-quality document set that balances precision and recall while capturing the user's core intent.
The overall objectives of the CLEF 2017 Dynamic Search Lab are described, the resources created for the pilot task, the resources created for the pilot task and the evaluation methodology adopted.
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 quer...
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 or...
This paper presents the RecSys Challenge 2026 challenge task, dataset, evaluation protocol, and official results, and identifies limitations of the benchmark and evaluation protocol, including single-ground-truth relevance and teacher-forced evaluation of synthetic dialogues.
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.
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