research-article

Warming Up to Cold Start Personalization

Authors:

Nikola Banovic,

John KrummAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 1, Issue 4

Article No.: 124, Pages 1 - 13

https://doi.org/10.1145/3161175

Published: 08 January 2018 Publication History

Abstract

Smart agents face abandonment if they are unable to provide value to the users from the very first interaction. Existing smart agents take time to learn about new users before they can offer them personalized services. We present a method for learning personalization information about users quickly and without placing unnecessary hardship on them. Our method enables smart agents to pick which questions to ask the user when they first interact to maximize the agent's overall knowledge about the user. We demonstrate our method on two publically available US census datasets containing 172 user variables from 1,799,394 training and 1,618,489 testing users. The questions selected using our method improve the agent's accuracy when inferring information about future users, including information that they did not ask about. Our work enables smart agents that assist the user with personalized services soon after they start interacting.

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Cited By

Kovacevic NHolz CGross MWampfler R(2024)On Multimodal Emotion Recognition for Human-Chatbot Interaction in the WildProceedings of the 26th International Conference on Multimodal Interaction10.1145/3678957.3685759(12-21)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3678957.3685759
Kianpisheh MMariakakis ATruong K(2024)exHARProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435008:1(1-30)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643500
Asthana SIm JChen ZBanovic N(2024)"I know even if you don't tell me": Understanding Users' Privacy Preferences Regarding AI-based Inferences of Sensitive Information for PersonalizationProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642180(1-21)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642180
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Index Terms

Warming Up to Cold Start Personalization
1. Information systems
  1. World Wide Web
    1. Web searching and information discovery
      1. Personalization

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Model-based approach to collaborative filtering (CF), such as latent factor models, has improved both accuracy and efficiency of predictions on large and sparse dataset. However, most existing methods still face two major problems: (1) the ...

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 1, Issue 4

December 2017

1298 pages

EISSN:2474-9567

DOI:10.1145/3178157

Issue’s Table of Contents

Copyright © 2018 ACM.

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 ACM 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]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 January 2018

Accepted: 01 October 2017

Revised: 01 August 2017

Received: 01 February 2017

Published in IMWUT Volume 1, Issue 4

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Cited By

Kovacevic NHolz CGross MWampfler R(2024)On Multimodal Emotion Recognition for Human-Chatbot Interaction in the WildProceedings of the 26th International Conference on Multimodal Interaction10.1145/3678957.3685759(12-21)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3678957.3685759
Kianpisheh MMariakakis ATruong K(2024)exHARProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435008:1(1-30)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643500
Asthana SIm JChen ZBanovic N(2024)"I know even if you don't tell me": Understanding Users' Privacy Preferences Regarding AI-based Inferences of Sensitive Information for PersonalizationProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642180(1-21)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642180
Razavi RXue GAkpan I(2024)Predicting Sociodemographic Attributes from Mobile Usage Patterns: Applications and Privacy ImplicationsBig Data10.1089/big.2022.018212:3(213-228)Online publication date: 1-Jun-2024
https://doi.org/10.1089/big.2022.0182
Hiremath SPlötz T(2023)The Lifespan of Human Activity Recognition Systems for Smart HomesSensors10.3390/s2318772923:18(7729)Online publication date: 7-Sep-2023
https://doi.org/10.3390/s23187729
Wang ZChen SJi SPan ZMeng CZhang JLi TZheng Y(2023)Housing rental suggestion based on e-commerce dataKnowledge-Based Systems10.1016/j.knosys.2023.110474268(110474)Online publication date: May-2023
https://doi.org/10.1016/j.knosys.2023.110474
Quintana MSchiavon STartarini FKim JMiller C(2023)Cohort comfort models — Using occupant’s similarity to predict personal thermal preference with less dataBuilding and Environment10.1016/j.buildenv.2022.109685227(109685)Online publication date: Jan-2023
https://doi.org/10.1016/j.buildenv.2022.109685
Oinas-Kukkonen HPohjolainen SAgyei E(2022)Mitigating Issues With/of/for True PersonalizationFrontiers in Artificial Intelligence10.3389/frai.2022.8448175Online publication date: 26-Apr-2022
https://doi.org/10.3389/frai.2022.844817
Esanov BDahanayake A(2021)Virtual Campus Journey: Personalization vs CustomizationReliability and Statistics in Transportation and Communication10.1007/978-3-030-68476-1_76(823-836)Online publication date: 7-Feb-2021
https://doi.org/10.1007/978-3-030-68476-1_76
Ohn-Bar EGuerreiro JKitani KAsakawa C(2018)Variability in Reactions to Instructional Guidance during Smartphone-Based Assisted Navigation of Blind UsersProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/32649412:3(1-25)Online publication date: 18-Sep-2018
https://dl.acm.org/doi/10.1145/3264941

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