research-article

Sparse hidden-dynamics conditional random fields for user intent understanding

Authors:

Zheng ChenAuthors Info & Claims

WWW '11: Proceedings of the 20th international conference on World wide web

Pages 7 - 16

https://doi.org/10.1145/1963405.1963411

Published: 28 March 2011 Publication History

Abstract

Understanding user intent from her sequential search behaviors, i.e. predicting the intent of each user query in a search session, is crucial for modern Web search engines. However, due to the huge number of user behavior variables and coarse level intent labels defined by human editors, it is very difficult to directly model user behavioral dynamics or user intent dynamics in user search sessions. In this paper, we propose a novel Sparse Hidden-Dynamic Conditional Random Fields (SHDCRF) model for user intent learning from their search sessions. Through incorporating the proposed hidden state variables, SHDCRF aims to learn a substructure, i.e. a set of related hidden variables, for each intent label and they are used to model the intermediate dynamics between user intent labels and user behavioral variables. In addition, SHDCRF learns a sparse relation between the hidden variables and intent labels to make the hidden state variables explainable. Extensive experiment results, on real user search sessions from a popular commercial search engine show that the proposed SHDCRF model significantly outperforms in terms of intent prediction results that those classical solutions such as Support Vector Machine (SVM), Conditional Random Field (CRF) and Latnet-Dynamic Conditional Random Field (LDCRF).

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Cited By

Fetahu BCohen NHaramaty ELewin-Eytan LRokhlenko OMalmasi S(2024)Identifying Shopping Intent in Product QA for Proactive RecommendationsAdvances on Graph-Based Approaches in Information Retrieval10.1007/978-3-031-71382-8_3(25-40)Online publication date: 10-Oct-2024
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Huang XHou HSun M(2022)A novel temporal recommendation method based on user query topic evolutionKnowledge-Based Systems10.1016/j.knosys.2022.108239241(108239)Online publication date: Apr-2022
https://doi.org/10.1016/j.knosys.2022.108239
Papenmeier AKern DHienert DSliwa AAker AFuhr NScholer FThomas PElsweiler DJoho HKando NSmith C(2021)Dataset of Natural Language Queries for E-CommerceProceedings of the 2021 Conference on Human Information Interaction and Retrieval10.1145/3406522.3446043(307-311)Online publication date: 14-Mar-2021
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Index Terms

Sparse hidden-dynamics conditional random fields for user intent understanding
1. Information systems
  1. Information retrieval

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cover image ACM Other conferences

WWW '11: Proceedings of the 20th international conference on World wide web

March 2011

840 pages

ISBN:9781450306324

DOI:10.1145/1963405

General Chairs:
S. Sadagopan
IIIT-Bangalore, India
,
Krithi Ramamritham
IIT-Bombay, India
,
Arun Kumar
IBM Research, India
,
M. P. Ravindra
Infosys E & R, India
,
Program Chairs:
Elisa Bertino
Purdue University, USA
,
Ravi Kumar
Yahoo! Research, USA

Copyright © 2011 ACM.

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
The International Institute of Information Technology Bangalore: The International Institute of Information Technology Bangalore

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Published: 28 March 2011

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WWW '11

WWW '11: 20th International World Wide Web Conference

March 28 - April 1, 2011

Hyderabad, India

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Cited By

Fetahu BCohen NHaramaty ELewin-Eytan LRokhlenko OMalmasi S(2024)Identifying Shopping Intent in Product QA for Proactive RecommendationsAdvances on Graph-Based Approaches in Information Retrieval10.1007/978-3-031-71382-8_3(25-40)Online publication date: 10-Oct-2024
https://doi.org/10.1007/978-3-031-71382-8_3
Huang XHou HSun M(2022)A novel temporal recommendation method based on user query topic evolutionKnowledge-Based Systems10.1016/j.knosys.2022.108239241(108239)Online publication date: Apr-2022
https://doi.org/10.1016/j.knosys.2022.108239
Papenmeier AKern DHienert DSliwa AAker AFuhr NScholer FThomas PElsweiler DJoho HKando NSmith C(2021)Dataset of Natural Language Queries for E-CommerceProceedings of the 2021 Conference on Human Information Interaction and Retrieval10.1145/3406522.3446043(307-311)Online publication date: 14-Mar-2021
https://dl.acm.org/doi/10.1145/3406522.3446043
Déneckère RKornyshova EHug C(2021)A Framework for Comparative Analysis of Intention Mining ApproachesResearch Challenges in Information Science10.1007/978-3-030-75018-3_2(20-37)Online publication date: 8-May-2021
https://doi.org/10.1007/978-3-030-75018-3_2
Liu JToubia O(2018)A Semantic Approach for Estimating Consumer Content Preferences from Online Search QueriesMarketing Science10.1287/mksc.2018.111237:6(930-952)Online publication date: 1-Nov-2018
https://dl.acm.org/doi/10.1287/mksc.2018.1112
Ning YJia JWu ZLi RAn YWang YMeng HSingh SMarkovitch S(2017)Multi-task deep learning for user intention understanding in speech interaction systemsProceedings of the Thirty-First AAAI Conference on Artificial Intelligence10.5555/3298239.3298264(161-167)Online publication date: 4-Feb-2017
https://dl.acm.org/doi/10.5555/3298239.3298264
Höffner KWalter SMarx EUsbeck RLehmann JNgonga Ngomo A(2017)Survey on challenges of Question Answering in the Semantic WebSemantic Web10.3233/SW-1602478:6(895-920)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.3233/SW-160247
Xie HYang JChang CLiu L(2017)A statistical analysis approach to predict user's changing requirements for software service evolutionJournal of Systems and Software10.1016/j.jss.2017.06.071132:C(147-164)Online publication date: 1-Oct-2017
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Luong TTran TTruong QTruong TPhi TPhan X(2016)Learning to Filter User Explicit Intents in Online Vietnamese Social Media TextsIntelligent Information and Database Systems10.1007/978-3-662-49390-8_2(13-24)Online publication date: 2016
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Salehi SDu JAshman HYesilada YFarzan RHouben G(2015)Examining Personalization in Academic Web SearchProceedings of the 26th ACM Conference on Hypertext & Social Media10.1145/2700171.2791039(103-111)Online publication date: 24-Aug-2015
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