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20162026
most citedLet Me Do It For You: Towards LLM Empowered Recommendation via Tool Learning

40 citations · 95 across the 58 of their papers we have counts for

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Showing cs.IRShow all

36 papers · 1 filter

cs.IR2026

A Mechanistic Analysis of Gender Sensitivity in Dense Retrieval Models

Catherine Chen, Maarten de Rijke, Carsten Eickhoff

While gender bias in dense retrieval models is well documented, with prior work showing that models often score male-gendered documents higher than female or neutral variants, the…

cs.IR2026

Lost in Decoding? Reproducing and Stress-Testing the Look-Ahead Prior in Generative Retrieval

Kidist Amde Mekonnen, Yongkang Li, Yubao Tang +2

Generative retrieval (GR) ranks documents by autoregressively generating document identifiers. Because many GR methods rely on trie-constrained beam search, they are vulnerable to…

cs.IR2026

Cold-Starts in Generative Recommendation: A Reproducibility Study

Zhen Zhang, Jujia Zhao, Xinyu Ma +3

Cold-start recommendation remains a central challenge in dynamic, open-world platforms, requiring models to recommend for newly registered users (user cold-start) and to recommend…

cs.IR2026

Model Editing for New Document Integration in Generative Information Retrieval

Zhen Zhang, Zihan Wang, Xinyu Ma +6

Generative retrieval (GR) reformulates the Information Retrieval (IR) task as the generation of document identifiers (docIDs). Despite its promise, existing GR models exhibit poor…

cs.IR2026

Contrastive Learning for Diversity-Aware Product Recommendations in Retail

Vasileios Karlis, Ezgi Yıldırım, David Vos +1

Recommender systems often struggle with long-tail distributions and limited item catalog exposure, where a small subset of popular items dominates recommendations. This challenge i…

cs.IR2026

Orcheo: A Modular Full-Stack Platform for Conversational Search

Shaojie Jiang, Svitlana Vakulenko, Maarten de Rijke

Conversational search (CS) requires a complex software engineering pipeline that integrates query reformulation, ranking, and response generation. CS researchers currently face two…