skip to main content
10.1145/3488560.3498447acmconferencesArticle/Chapter ViewAbstractPublication PageswsdmConference Proceedingsconference-collections
research-article

Improving Personalized Search with Dual-Feedback Network

Published: 15 February 2022 Publication History

Abstract

Personalized search improves the quality of search results by modeling historical user behavior. In recent years, many methods based on deep learning have greatly improved the performance of personalized search. However, most of the existing methods only focus on modeling positive user behavior signals, which leads to incomplete user interest modeling. At the same time, the user's search behavior hides much explicit or implicit feedback information. For example, clicking and staying for a certain period represents implicit positive feedback, and skipping behavior represents implicit negative feedback. Intuitively, this information can be utilized to construct a more complete and accurate user profile. In this paper, we propose a dual-feedback modeling framework, which integrates multi-granular user feedback information to model the user's current search intention. Specifically, we propose a feedback extraction network to refine the dual-feedback representation in multiple stages. For enhancing the user's real-time search quality, we design an additional dual-feedback feature gating module to capture the user's real-time feedback in the current session. We conducted a large number of experiments on two real-world datasets, and the experimental results show that our method can effectively improve the performance of personalized search.

Supplementary Material

MP4 File (WSDM22-fp376.mp4)
Presentation video

References

[1]
Wasi Uddin Ahmad, Kai-Wei Chang, and Hongning Wang. 2018. Multi-task learning for document ranking and query suggestion. In International Conference on Learning Representations.
[2]
Paul N Bennett, Krysta Svore, and Susan T Dumais. 2010. Classification-enhanced ranking. In Proceedings of the 19th international conference on World wide web. 111--120.
[3]
Paul N Bennett, Ryen W White, Wei Chu, Susan T Dumais, Peter Bailey, Fedor Borisyuk, and Xiaoyuan Cui. 2012. Modeling the impact of short-and long-term behavior on search personalization. In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval. 185--194.
[4]
Fei Cai, Shangsong Liang, and Maarten De Rijke. 2014. Personalized document re-ranking based on bayesian probabilistic matrix factorization. In Proceedings of the SIGIR'2014. ACM, 835--838.
[5]
Mark J Carman, Fabio Crestani, Morgan Harvey, and Mark Baillie. 2010. Towards query log based personalization using topic models. In Proceedings of the 19th ACM international conference on Information and knowledge management. 1849-- 1852.
[6]
Kyunghyun Cho, Bart van Merriënboer Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares Holger Schwenk, and Yoshua Bengio. [n.d.]. Learning Phrase Representations using RNN Encoder--Decoder for Statistical Machine Translation. ([n. d.]).
[7]
Kevyn Collins-Thompson, Paul N Bennett, RyenWWhite, Sebastian De La Chica, and David Sontag. 2011. Personalizing web search results by reading level. In Proceedings of the 20th ACM international conference on Information and knowledge management. 403--412.
[8]
Zhuyun Dai, Chenyan Xiong, Jamie Callan, and Zhiyuan Liu. 2018. Convolutional neural networks for soft-matching n-grams in ad-hoc search. In Proceedings of the eleventh ACM international conference on web search and data mining. 126--134.
[9]
Zhicheng Dou, Ruihua Song, and Ji-RongWen. 2007. A large-scale evaluation and analysis of personalized search strategies. In Proceedings of the 16th international conference on World Wide Web. 581--590.
[10]
Songwei Ge, Zhicheng Dou, Zhengbao Jiang, Jian-Yun Nie, and Ji-Rong Wen. 2018. Personalizing search results using hierarchical RNN with query-aware attention. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 347--356.
[11]
Felix A Gers, Nicol N Schraudolph, and Jürgen Schmidhuber. 2002. Learning precise timing with LSTM recurrent networks. Journal of machine learning research 3, Aug (2002), 115--143.
[12]
Shufeng Hao, Chongyang Shi, Zhendong Niu, and Longbing Cao. 2019. Modeling positive and negative feedback for improving document retrieval. Expert Systems with Applications 120 (2019), 253--261.
[13]
Morgan Harvey, Fabio Crestani, and Mark J Carman. 2013. Building user profiles from topic models for personalised search. In Proceedings of the 22nd ACM international conference on Information & Knowledge Management. 2309--2314.
[14]
Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735--1780.
[15]
Thorsten Joachims, Laura Granka, Bing Pan, Helene Hembrooke, and Geri Gay. 2017. Accurately interpreting clickthrough data as implicit feedback. In ACM SIGIR Forum, Vol. 51. Acm New York, NY, USA, 4--11.
[16]
Lin Li, Zhenglu Yang, Botao Wang, and Masaru Kitsuregawa. 2007. Dynamic adaptation strategies for long-term and short-term user profile to personalize search. In Advances in Data and Web Management. Springer, 228--240.
[17]
Shuqi Lu, Zhicheng Dou, Xu Jun, Jian-Yun Nie, and Ji-Rong Wen. 2019. Psgan: A minimax game for personalized search with limited and noisy click data. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 555--564.
[18]
Zhengyi Ma, Zhicheng Dou, Guanyue Bian, and Ji-Rong Wen. 2020. PSTIE: Time Information Enhanced Personalized Search. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 1075--1084.
[19]
Greg Pass, Abdur Chowdhury, and Cayley Torgeson. 2006. A picture of search. In Proceedings of the 1st international conference on Scalable information systems. 1--es.
[20]
Yifan Qiao, Chenyan Xiong, Zhenghao Liu, and Zhiyuan Liu. 2019. Understanding the Behaviors of BERT in Ranking. arXiv preprint arXiv:1904.07531 (2019).
[21]
Stephen Robertson and Hugo Zaragoza. 2009. The probabilistic relevance framework: BM25 and beyond. Now Publishers Inc.
[22]
Ahu Sieg, Bamshad Mobasher, and Robin Burke. 2007. Web search personalization with ontological user profiles. In Proceedings of the sixteenth ACM conference on Conference on information and knowledge management. 525--534.
[23]
Jaime Teevan, Susan T Dumais, and Daniel J Liebling. 2008. To personalize or not to personalize: modeling queries with variation in user intent. In Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval. 163--170.
[24]
Jaime Teevan, Daniel J Liebling, and Gayathri Ravichandran Geetha. 2011. Understanding and predicting personal navigation. In Proceedings of the fourth ACM international conference on Web search and data mining. 85--94.
[25]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, and Illia Polosukhin. 2017. Attention is all you need. In Advances in neural information processing systems. 5998--6008.
[26]
Maksims Volkovs. 2015. Context models for web search personalization. arXiv preprint arXiv:1502.00527 (2015).
[27]
Thanh Vu, Dat Quoc Nguyen, Mark Johnson, Dawei Song, and Alistair Willis. 2017. Search personalization with embeddings. In European Conference on Information Retrieval. Springer, 598--604.
[28]
Thanh Vu, Alistair Willis, Son N Tran, and Dawei Song. 2015. Temporal latent topic user profiles for search personalisation. In European Conference on Information Retrieval. Springer, 605--616.
[29]
Thanh Tien Vu, Dawei Song, Alistair Willis, Son Ngoc Tran, and Jingfei Li. 2014. Improving search personalisation with dynamic group formation. In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval. 951--954.
[30]
Hongning Wang, Xiaodong He, Ming-Wei Chang, Yang Song, Ryen W White, and Wei Chu. 2013. Personalized ranking model adaptation for web search. In Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval. 323--332.
[31]
Xuanhui Wang, Hui Fang, and ChengXiang Zhai. 2008. A study of methods for negative relevance feedback. In Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval. 219--226.
[32]
RyenWWhite, Paul N Bennett, and Susan T Dumais. 2010. Predicting short-term interests using activity-based search context. In Proceedings of the 19th ACM international conference on Information and knowledge management. 1009--1018.
[33]
Ryen W White, Wei Chu, Ahmed Hassan, Xiaodong He, Yang Song, and Hongning Wang. 2013. Enhancing personalized search by mining and modeling task behavior. In Proceedings of the 22nd international conference on World Wide Web. 1411--1420.
[34]
Ryen W White and Steven M Drucker. 2007. Investigating behavioral variability in web search. In Proceedings of the 16th international conference on World Wide Web. 21--30.
[35]
Chuhan Wu, Fangzhao Wu, Tao Qi, and Yongfeng Huang. 2021. FeedRec: News Feed Recommendation with Various User Feedbacks. arXiv preprint arXiv:2102.04903 (2021).
[36]
Ruobing Xie, Cheng Ling, Yalong Wang, Rui Wang, Feng Xia, and Leyu Lin. 2020. Deep Feedback Network for Recommendation. In IJCAI. 2519--2525.
[37]
Chenyan Xiong, Zhuyun Dai, Jamie Callan, Zhiyuan Liu, and Russell Power. 2017. End-to-end neural ad-hoc ranking with kernel pooling. In Proceedings of the 40th International ACM SIGIR conference on research and development in information retrieval. 55--64.
[38]
Jing Yao, Zhicheng Dou, and Ji-Rong Wen. 2020. Employing Personal Word Embeddings for Personalized Search. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 1359--1368.
[39]
Jing Yao, Zhicheng Dou, Jun Xu, and Ji-RongWen. 2020. RLPer: A Reinforcement Learning Model for Personalized Search. In Proceedings of The Web Conference 2020. 2298--2308.
[40]
Wen-jing Zhang and Jun-yi Wang. 2012. The study of methods for language model based positive and negative relevance feedback in information retrieval. In 2012 Fourth International Symposium on Information Science and Engineering. IEEE, 39--43.
[41]
Yujia Zhou, Zhicheng Dou, Bingzheng Wei, Ruobing Xie, and Ji-Rong Wen. 2021. Group based Personalized Search by Integrating Search Behaviour and Friend Network. In SIGIR. ACM, 92--101.
[42]
Yujia Zhou, Zhicheng Dou, and Ji-Rong Wen. 2020. Encoding History with Context-aware Representation Learning for Personalized Search. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 1111--1120.
[43]
Yujia Zhou, Zhicheng Dou, and Ji-Rong Wen. 2020. Enhancing Re-finding Behavior with External Memories for Personalized Search. In Proceedings of the 13th International Conference on Web Search and Data Mining. 789--797.
[44]
Yujia Zhou, Zhicheng Dou, and Ji-Rong Wen. 2021. Enhancing Potential Refinding in Personalized Search with Hierarchical Memory Networks. IEEE Transactions on Knowledge and Data Engineering (2021).
[45]
Yujia Zhou, Zhicheng Dou, Yutao Zhu, and Ji-Rong Wen. 2021. PSSL: Selfsupervised Learning for Personalized Search with Contrastive Sampling. In CIKM. ACM, 2749--2758.

Cited By

View all
  • (2024)Intent-Oriented Dynamic Interest Modeling for Personalized Web SearchACM Transactions on Information Systems10.1145/363981742:4(1-30)Online publication date: 9-Feb-2024
  • (2024)Long short-term search session-based document re-ranking modelKnowledge and Information Systems10.1007/s10115-024-02205-467:1(223-245)Online publication date: 9-Sep-2024
  • (2024)How to personalize and whether to personalize? Candidate documents decideKnowledge and Information Systems10.1007/s10115-024-02138-y66:9(5581-5604)Online publication date: 27-May-2024
  • Show More Cited By

Index Terms

  1. Improving Personalized Search with Dual-Feedback Network

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining
    February 2022
    1690 pages
    ISBN:9781450391320
    DOI:10.1145/3488560
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 15 February 2022

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. dual-feedback network
    2. personalized search
    3. user intention

    Qualifiers

    • Research-article

    Funding Sources

    Conference

    WSDM '22

    Acceptance Rates

    Overall Acceptance Rate 498 of 2,863 submissions, 17%

    Upcoming Conference

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)53
    • Downloads (Last 6 weeks)2
    Reflects downloads up to 13 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)Intent-Oriented Dynamic Interest Modeling for Personalized Web SearchACM Transactions on Information Systems10.1145/363981742:4(1-30)Online publication date: 9-Feb-2024
    • (2024)Long short-term search session-based document re-ranking modelKnowledge and Information Systems10.1007/s10115-024-02205-467:1(223-245)Online publication date: 9-Sep-2024
    • (2024)How to personalize and whether to personalize? Candidate documents decideKnowledge and Information Systems10.1007/s10115-024-02138-y66:9(5581-5604)Online publication date: 27-May-2024
    • (2023)Integrated Personalized and Diversified Search Based on Search LogsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.329100636:2(694-707)Online publication date: 30-Jun-2023
    • (2023)A Topicality Relevance-Aware Intent Model for Web SearchIEEE Access10.1109/ACCESS.2023.328982011(65739-65748)Online publication date: 2023

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media