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Contrastive Learning of User Behavior Sequence for Context-Aware Document Ranking
上下文感知文档排序中用户行为序列的对比学习
文脈意識型文書ランキングのためのユーザ行動シーケンスの対照学習
문맥 감지 문서 정렬 에서 사용자 행동 시퀀스 의 비교 학습
Estudio comparativo de las secuencias de comportamiento de los usuarios en la clasificación de documentos sensibles al contexto
Étude comparative des séquences de comportement des utilisateurs dans le tri des documents sensibles au contexte
контекстное восприятие документа сортировка поведение пользователя
Yutao Zhu ¹, Jian-Yun Nie 聂建云 ¹, Zhicheng Dou 窦志成 ², Zhengyi Ma 马正一 ², Xinyu Zhang 张鑫宇 ³, Pan Du ¹, Xiaochen Zuo 左笑晨 ², Hao Jiang 蒋昊 ³
¹ Université de Montréal, Montréal, Québec, Canada
² Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China
中国 北京 中国人民大学高瓴人工智能学院
³ Distributed and Parallel Software Lab, Huawei, Hangzhou, Zhejiang, China
中国 浙江 杭州 华为分布式与并行软件实验室
arXiv, 24 August 2021
Abstract

Context information in search sessions has proven to be useful for capturing user search intent. Existing studies explored user behavior sequences in sessions in different ways to enhance query suggestion or document ranking. However, a user behavior sequence has often been viewed as a definite and exact signal reflecting a user's behavior. In reality, it is highly variable: user's queries for the same intent can vary, and different documents can be clicked.

To learn a more robust representation of the user behavior sequence, we propose a method based on contrastive learning, which takes into account the possible variations in user's behavior sequences. Specifically, we propose three data augmentation strategies to generate similar variants of user behavior sequences and contrast them with other sequences. In so doing, the model is forced to be more robust regarding the possible variations. The optimized sequence representation is incorporated into document ranking.

Experiments on two real query log datasets show that our proposed model outperforms the state-of-the-art methods significantly, which demonstrates the effectiveness of our method for context-aware document ranking.
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