@inproceedings{shijun-etal-2024-enhancing,
title = "Enhancing Sequence Representation for Personalized Search",
author = "Shijun, Wang and
Han, Zhang and
Zhe, Yuan",
editor = "Sun, Maosong and
Liang, Jiye and
Han, Xianpei and
Liu, Zhiyuan and
He, Yulan",
booktitle = "Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)",
month = jul,
year = "2024",
address = "Taiyuan, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2024.ccl-1.76/",
pages = "986--998",
language = "eng",
abstract = "{\textquotedblleft}The critical process of personalized search is to reorder candidate documents of the current querybased on the user`s historical behavior sequence. There are many types of information containedin user historical information sequence, such as queries, documents, and clicks. Most existingpersonalized search approaches concatenate these types of information to get an overall userrepresentation, but they ignore the associations among them. We believe the associations ofdifferent information mentioned above are significant to personalized search. Based on a hierar-chical transformer as base architecture, we design three auxiliary tasks to capture the associationsof different information in user behavior sequence. Under the guidance of mutual information,we adjust the training loss, enabling our PSMIM model to better enhance the information rep-resentation in personalized search. Experimental results demonstrate that our proposed methodoutperforms some personalized search methods.{\textquotedblright}"
}
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<abstract>“The critical process of personalized search is to reorder candidate documents of the current querybased on the user‘s historical behavior sequence. There are many types of information containedin user historical information sequence, such as queries, documents, and clicks. Most existingpersonalized search approaches concatenate these types of information to get an overall userrepresentation, but they ignore the associations among them. We believe the associations ofdifferent information mentioned above are significant to personalized search. Based on a hierar-chical transformer as base architecture, we design three auxiliary tasks to capture the associationsof different information in user behavior sequence. Under the guidance of mutual information,we adjust the training loss, enabling our PSMIM model to better enhance the information rep-resentation in personalized search. Experimental results demonstrate that our proposed methodoutperforms some personalized search methods.”</abstract>
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%0 Conference Proceedings
%T Enhancing Sequence Representation for Personalized Search
%A Shijun, Wang
%A Han, Zhang
%A Zhe, Yuan
%Y Sun, Maosong
%Y Liang, Jiye
%Y Han, Xianpei
%Y Liu, Zhiyuan
%Y He, Yulan
%S Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)
%D 2024
%8 July
%I Chinese Information Processing Society of China
%C Taiyuan, China
%G eng
%F shijun-etal-2024-enhancing
%X “The critical process of personalized search is to reorder candidate documents of the current querybased on the user‘s historical behavior sequence. There are many types of information containedin user historical information sequence, such as queries, documents, and clicks. Most existingpersonalized search approaches concatenate these types of information to get an overall userrepresentation, but they ignore the associations among them. We believe the associations ofdifferent information mentioned above are significant to personalized search. Based on a hierar-chical transformer as base architecture, we design three auxiliary tasks to capture the associationsof different information in user behavior sequence. Under the guidance of mutual information,we adjust the training loss, enabling our PSMIM model to better enhance the information rep-resentation in personalized search. Experimental results demonstrate that our proposed methodoutperforms some personalized search methods.”
%U https://aclanthology.org/2024.ccl-1.76/
%P 986-998
Markdown (Informal)
[Enhancing Sequence Representation for Personalized Search](https://aclanthology.org/2024.ccl-1.76/) (Shijun et al., CCL 2024)
ACL
- Wang Shijun, Zhang Han, and Yuan Zhe. 2024. Enhancing Sequence Representation for Personalized Search. In Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference), pages 986–998, Taiyuan, China. Chinese Information Processing Society of China.