research-article

Minimal Interaction Content Discovery in Recommender Systems

Authors:

Branislav Kveton,

Shlomo BerkovskyAuthors Info & Claims

ACM Transactions on Interactive Intelligent Systems (TiiS), Volume 6, Issue 2

Article No.: 15, Pages 1 - 25

https://doi.org/10.1145/2845090

Published: 20 July 2016 Publication History

Abstract

Many prior works in recommender systems focus on improving the accuracy of item rating predictions. In comparison, the areas of recommendation interfaces and user-recommender interaction remain underexplored. In this work, we look into the interaction of users with the recommendation list, aiming to devise a method that simplifies content discovery and minimizes the cost of reaching an item of interest. We quantify this cost by the number of user interactions (clicks and scrolls) with the recommendation list. To this end, we propose generalized linear search (GLS), an adaptive combination of the established linear and generalized search (GS) approaches. GLS leverages the advantages of these two approaches, and we prove formally that it performs at least as well as GS. We also conduct a thorough experimental evaluation of GLS and compare it to several baselines and heuristic approaches in both an offline and live evaluation. The results of the evaluation show that GLS consistently outperforms the baseline approaches and is also preferred by users. In summary, GLS offers an efficient and easy-to-use means for content discovery in recommender systems.

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Cited By

Lam KLee LHui PShen HZhuang YSmith JYang YCesar PMetze FPrabhakaran B(2021)A2W: Context-Aware Recommendation System for Mobile Augmented Reality Web BrowserProceedings of the 29th ACM International Conference on Multimedia10.1145/3474085.3475413(2447-2455)Online publication date: 17-Oct-2021
https://dl.acm.org/doi/10.1145/3474085.3475413
Lee LBraud THosio SHui P(2021)Towards Augmented Reality Driven Human-City Interaction: Current Research on Mobile Headsets and Future ChallengesACM Computing Surveys10.1145/346796354:8(1-38)Online publication date: 4-Oct-2021
https://dl.acm.org/doi/10.1145/3467963
Zhang YCoecke BChen M(2021)MI3: Machine-initiated Intelligent Interaction for Interactive Classification and Data ReconstructionACM Transactions on Interactive Intelligent Systems10.1145/341284811:3-4(1-34)Online publication date: 3-Sep-2021
https://dl.acm.org/doi/10.1145/3412848
Show More Cited By

Index Terms

Minimal Interaction Content Discovery in Recommender Systems
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
    2. Interactive systems and tools
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Active learning

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Information & Contributors

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Published In

cover image ACM Transactions on Interactive Intelligent Systems

ACM Transactions on Interactive Intelligent Systems Volume 6, Issue 2

Regular Articles, Special Issue on Highlights of IUI 2015 (Part 2 of 2) and Special Issue on Highlights of ICMI 2014 (Part 1 of 2)

August 2016

282 pages

ISSN:2160-6455

EISSN:2160-6463

DOI:10.1145/2974721

Editor:
Michelle Zhou
Juji, Inc., USA

Issue’s Table of Contents

Copyright © 2016 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: 20 July 2016

Accepted: 01 March 2016

Revised: 01 December 2015

Received: 01 July 2015

Published in TIIS Volume 6, Issue 2

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Cited By

Lam KLee LHui PShen HZhuang YSmith JYang YCesar PMetze FPrabhakaran B(2021)A2W: Context-Aware Recommendation System for Mobile Augmented Reality Web BrowserProceedings of the 29th ACM International Conference on Multimedia10.1145/3474085.3475413(2447-2455)Online publication date: 17-Oct-2021
https://dl.acm.org/doi/10.1145/3474085.3475413
Lee LBraud THosio SHui P(2021)Towards Augmented Reality Driven Human-City Interaction: Current Research on Mobile Headsets and Future ChallengesACM Computing Surveys10.1145/346796354:8(1-38)Online publication date: 4-Oct-2021
https://dl.acm.org/doi/10.1145/3467963
Zhang YCoecke BChen M(2021)MI3: Machine-initiated Intelligent Interaction for Interactive Classification and Data ReconstructionACM Transactions on Interactive Intelligent Systems10.1145/341284811:3-4(1-34)Online publication date: 3-Sep-2021
https://dl.acm.org/doi/10.1145/3412848
Zou JKanoulas E(2020)Towards Question-based High-recall Information RetrievalACM Transactions on Information Systems10.1145/338864038:3(1-35)Online publication date: 18-May-2020
https://dl.acm.org/doi/10.1145/3388640
Berkovsky STaib RHijikata YBraslavsku PKnijnenburg BMitrovic TZhang JChen LChin D(2018)A Cross-Cultural Analysis of Trust in Recommender SystemsProceedings of the 26th Conference on User Modeling, Adaptation and Personalization10.1145/3209219.3209251(285-289)Online publication date: 3-Jul-2018
https://dl.acm.org/doi/10.1145/3209219.3209251
Devaraju ABerkovsky SMitrovic TZhang JChen LChin D(2018)A Hybrid Recommendation Approach for Open Research DatasetsProceedings of the 26th Conference on User Modeling, Adaptation and Personalization10.1145/3209219.3209250(207-211)Online publication date: 3-Jul-2018
https://dl.acm.org/doi/10.1145/3209219.3209250

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