This paper proposes HyCoRec, a method to alleviate the Matthew effect in conversational recommendation by learning multi-aspect preferences.

This paper proposes a framework for multi-hop retrieval as query-set compatibility scoring, improving retrieval performance and downstream QA task performance.

This paper introduces a new way to represent finite posets as subwords of finite words in categories, and characterizes the monic categories that admit this representation.

The paper proposes DINOSAUR, a framework that incorporates embedding uncertainty into Approximate Nearest Neighbour search to improve retrieval for niche, long-tail content.

This paper presents a simpler variation of the NC detection criterion for perfect matchings in bipartite graphs with improved parameters.

SkillPager is a novel two-stage framework that efficiently selects minimal, execution-sufficient context from large procedural skill documents by leveraging typed semantic nodes, significantly reducin…

The paper introduces an Item Response Theory (IRT)-based indicator that effectively identifies likely mislabeled items in existing LLM benchmarks, revealing systematic errors in labeling and model spe…

The Cologne Information Retrieval group participated in iKAT SCAI 2026 shared task using an agentic conversational search system with query rewriting, retrieval, reranking, answer generation, and clar…

This paper studies six metadata-generation settings for RDF datasets and evaluates their effectiveness and faithfulness in dataset search.

This paper proposes ShopX, a model-centric framework for intent-driven shopping experiences using a single foundation model for intent understanding, execution planning, and item-space operations.

The authors propose a new solution for the content cold-start problem in industry-scale search and recommender systems, reducing bias, improving model prediction, and validating long-term impact.

The paper conducts a reproducibility study on FACTER, a model-agnostic framework for fairness and statistical coverage in LLM-based recommendation, and evaluates its consistency and contribution.

The paper introduces TaDaS, a framework that analyzes large-scale text archives to measure professional sentiment, finding that while AI discussion among economists is initially negative, the trend sh…

This paper investigates the effectiveness of stage-dependent preference elicitation strategies in conversational recommendation systems and introduces COPE, a novel architecture for strategy modeling.

This paper proposes TSGR, a generative retrieval framework for industrial e-commerce search that incorporates value awareness into item representation and candidate ranking.

This paper proposes a multi-turn retrieval-augmented generation pipeline for conversational systems across four domains.

The paper presents BEATS, a human-in-the-loop LLM framework for bootstrapping product attribute taxonomies from scratch.