research-article

aDMSCN: A Novel Perspective for User Intent Prediction in Customer Service Bots

Authors:

Yu QiaoAuthors Info & Claims

CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

Pages 2853 - 2860

https://doi.org/10.1145/3340531.3412683

Published: 19 October 2020 Publication History

Abstract

As one of the core components of customer service bot, User Intent Prediction (UIP) aims at predicting users? intents (usually represented as predefined user questions) before they ask, and has been widely applied in real applications. However, when developing a machine learning system for this problem, two critical issues, i.e., the problem of feature drift and class imbalance, may emerge and seriously deprave the system performance. Moreover, various scenarios may arise due to business demands, making the aforementioned problems much more severe. To address these two problems, we propose an attention-based Deep Multi-instance Sequential Cross Network (aDMSCN) to deal with the UIP task. On the one hand,the UIP task can be subtly formalized as multi-instance learning(MIL) task with an attention-based method proposed to alleviate the influences of feature drift. To the best of our knowledge, this is the first attempt to model the problem from a MIL perspective.On the other hand, a ratio-sensitive loss is also developed in our model, which can mitigate the negative impact of class imbalance. Extensive experiments on both offline real-world datasets and on-line A/B testing show that our proposed framework significantly out performs other state-of-art methods for the UIP task.

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An introduction to a novel perspective when dealing with user intent prediction for practical online service bots.

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Zhang YZhou JRen YZhang YYang XLi MShi QLi L(2023)ALT: An Automatic System for Long Tail Scenario Modeling2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00231(3017-3030)Online publication date: Apr-2023
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Index Terms

aDMSCN: A Novel Perspective for User Intent Prediction in Customer Service Bots
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

October 2020

3619 pages

ISBN:9781450368599

DOI:10.1145/3340531

General Chairs:
Mathieu d'Aquin
DSI, Insight, NUI Galway, Ireland
,
Stefan Dietze
GESIS, Cologne, Germany, Heinrich-Heine-University Düsseldorf, Germany, L3S Research Center, Germany
,
Program Chairs:
Claudia Hauff
TU Delft, The Netherlands
,
Edward Curry
DSI, Insight, NUI Galway, Ireland
,
Philippe Cudre Mauroux
eXascale, University of Fribourg, Switzerland

Copyright © 2020 ACM.

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Published: 19 October 2020

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October 19 - 23, 2020

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Cited By

Zhang YZhou JRen YZhang YYang XLi MShi QLi L(2023)ALT: An Automatic System for Long Tail Scenario Modeling2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00231(3017-3030)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00231
Zhou JChen CLi LZhang ZZheng X(2022)FinBrain 2.0: when finance meets trustworthy AI金融大脑2.0：当金融遇到可信人工智能Frontiers of Information Technology & Electronic Engineering10.1631/FITEE.220003923:12(1747-1764)Online publication date: 30-Sep-2022
https://doi.org/10.1631/FITEE.2200039
Hussain HTamizharasan PRahul C(2022)Design possibilities and challenges of DNN models: a review on the perspective of end devicesArtificial Intelligence Review10.1007/s10462-022-10138-z55:7(5109-5167)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s10462-022-10138-z
Zhang YShi QLi MYang XLi LZhou J(2021)A Classification Based Ensemble Pruning Framework with Multi-metric ConsiderationIntelligent Systems and Applications10.1007/978-3-030-82193-7_44(650-667)Online publication date: 4-Aug-2021
https://doi.org/10.1007/978-3-030-82193-7_44

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