(Preprint) Contrastive Learning of User Behavior Sequence for Context-Aware Document Ranking
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, 2021-08-24
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.
High-resolution tumor marker detection based on microwave photonics demodulated dual wavelength fiber laser sensor
Jie Hu, Weihao Lin, Liyang Shao, Chenlong Xue, Fang Zhao, Dongrui Xiao, Yang Ran, Yue Meng, Panpan He, Zhiguang Yu, Jinna Chen, Perry Ping Shum
Opto-Electronic Advances
2024-12-16
Ultra-high-Q photonic crystal nanobeam cavity for etchless lithium niobate on insulator (LNOI) platform
Zhi Jiang, Cizhe Fang, Xu Ran, Yu Gao, Ruiqing Wang, Jianguo Wang, Danyang Yao, Xuetao Gan, Yan Liu, Yue Hao, Genquan Han
Opto-Electronic Advances
2024-10-31