arXiv:2304.12650
공식 코드 · Papers with Code
THUIR at WSDM Cup 2023 Task 1: Unbiased Learning to Rank
원문 초록
This paper introduces the approaches we have used to participate in the WSDM Cup 2023 Task 1: Unbiased Learning to Rank. In brief, we have attempted a combination of both traditional IR models and transformer-based cross-encoder architectures. To further enhance the ranking performance, we also considered a series of features for learning to rank. As a result, we won 2nd place on the final leaderboard.
함께 살펴볼 논문
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Kamae: Bridging Spark and Keras for Seamless ML Preprocessing
Unidentified and Confounded? Understanding Two-Tower Models for Unbiased Learning to Rank
LTRR: Learning To Rank Retrievers for LLMs
Paths to Causality: Finding Informative Subgraphs Within Knowledge Graphs for Knowledge-Based Causal Discovery
VisualQuality-R1: Reasoning-Induced Image Quality Assessment via Reinforcement Learning to Rank
Unlearning for Federated Online Learning to Rank: A Reproducibility Study
토론
이 논문과 연결된 포럼 글이 아직 없습니다.