research-article

Learning Context-dependent Personal Preferences for Adaptive Recommendation

Authors:

Hiroki Tsuchida,

Kris KitaniAuthors Info & Claims

ACM Transactions on Interactive Intelligent Systems (TiiS), Volume 10, Issue 3

Article No.: 23, Pages 1 - 26

https://doi.org/10.1145/3359755

Published: 09 November 2020 Publication History

Abstract

We propose two online-learning algorithms for modeling the personal preferences of users of interactive systems. The proposed algorithms leverage user feedback to estimate user behavior and provide personalized adaptive recommendation for supporting context-dependent decision-making. We formulate preference modeling as online prediction algorithms over a set of learned policies, i.e., policies generated via supervised learning with interaction and context data collected from previous users. The algorithms then adapt to a target user by learning the policy that best predicts that user’s behavior and preferences. We also generalize the proposed algorithms for a more challenging learning case in which they are restricted to a limited number of trained policies at each timestep, i.e., for mobile settings with limited resources. While the proposed algorithms are kept general for use in a variety of domains, we developed an image-filter-selection application. We used this application to demonstrate how the proposed algorithms can quickly learn to match the current user’s selections. Based on these evaluations, we show that (1) the proposed algorithms exhibit better prediction accuracy compared to traditional supervised learning and bandit algorithms, (2) our algorithms are robust under challenging limited prediction settings in which a smaller number of expert policies is assumed. Finally, we conducted a user study to demonstrate how presenting users with the prediction results of our algorithms significantly improves the efficiency of the overall interaction experience.

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Cited By

Jindal PGouri H(2024)AI-Personalization ParadoxAI Impacts in Digital Consumer Behavior10.4018/979-8-3693-1918-5.ch004(82-111)Online publication date: 1-Mar-2024
https://doi.org/10.4018/979-8-3693-1918-5.ch004
Ozeki SKotera MIshiguro KNishimura THiguchi KYamakata YHashimoto AChen J(2022)Recipe Recommendation for Balancing Ingredient Preference and Daily NutrientsProceedings of the 1st International Workshop on Multimedia for Cooking, Eating, and related APPlications10.1145/3552485.3554941(11-19)Online publication date: 10-Oct-2022
https://dl.acm.org/doi/10.1145/3552485.3554941

Index Terms

Learning Context-dependent Personal Preferences for Adaptive Recommendation
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
2. Human-centered computing
  1. Human computer interaction (HCI)
  2. Ubiquitous and mobile computing

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Published In

cover image ACM Transactions on Interactive Intelligent Systems

ACM Transactions on Interactive Intelligent Systems Volume 10, Issue 3

Special Issue on Data-Driven Personality Modeling for Intelligent Human-Computer Interaction

September 2020

189 pages

ISSN:2160-6455

EISSN:2160-6463

DOI:10.1145/3430388

Editor:
Michelle X. Zhou
Juji, Inc., USA

Issue’s Table of Contents

Copyright © 2020 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: 09 November 2020

Online AM: 07 May 2020

Accepted: 01 April 2020

Revised: 01 April 2020

Received: 01 February 2019

Published in TIIS Volume 10, Issue 3

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Online preference modeling; Context-dependent decision-making

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Cited By

Jindal PGouri H(2024)AI-Personalization ParadoxAI Impacts in Digital Consumer Behavior10.4018/979-8-3693-1918-5.ch004(82-111)Online publication date: 1-Mar-2024
https://doi.org/10.4018/979-8-3693-1918-5.ch004
Ozeki SKotera MIshiguro KNishimura THiguchi KYamakata YHashimoto AChen J(2022)Recipe Recommendation for Balancing Ingredient Preference and Daily NutrientsProceedings of the 1st International Workshop on Multimedia for Cooking, Eating, and related APPlications10.1145/3552485.3554941(11-19)Online publication date: 10-Oct-2022
https://dl.acm.org/doi/10.1145/3552485.3554941

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