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6 papers · 2 filters
PCR-CA: Parallel Codebook Representations with Contrastive Alignment for Multiple-Category App Recommendation
Bin Tan, Wangyao Ge, Yidi Wang +4
Modern app store recommender systems struggle with multiple-category apps, as traditional taxonomies fail to capture overlapping semantics, leading to suboptimal personalization. W…
Harnessing the Power of Interleaving and Counterfactual Evaluation for Airbnb Search Ranking
Qing Zhang, Alex Deng, Michelle Du +3
Evaluation plays a crucial role in the development of ranking algorithms on search and recommender systems. It enables online platforms to create user-friendly features that drive…
Overview of the TREC 2022 deep learning track
Nick Craswell, Bhaskar Mitra, Emine Yilmaz +4
This is the fourth year of the TREC Deep Learning track. As in previous years, we leverage the MS MARCO datasets that made hundreds of thousands of human annotated training labels…
Overview of the TREC 2021 deep learning track
Nick Craswell, Bhaskar Mitra, Emine Yilmaz +2
This is the third year of the TREC Deep Learning track. As in previous years, we leverage the MS MARCO datasets that made hundreds of thousands of human annotated training labels a…
LLM-Evaluation Tropes: Perspectives on the Validity of LLM-Evaluations
Laura Dietz, Oleg Zendel, Peter Bailey +6
Large Language Models (LLMs) are increasingly used to evaluate information retrieval (IR) systems, generating relevance judgments traditionally made by human assessors. Recent empi…
LLMs can be Fooled into Labelling a Document as Relevant (best café near me; this paper is perfectly relevant)
Marwah Alaofi, Paul Thomas, Falk Scholer +1
LLMs are increasingly being used to assess the relevance of information objects. This work reports on experiments to study the labelling of short texts (i.e., passages) for relevan…