research-article

Where can my career take me?: harnessing dialogue for interactive career goal recommendations

Authors:

Elizabeth M. Daly,

Abel N. Valente,

Pablo PedemonteAuthors Info & Claims

IUI '19: Proceedings of the 24th International Conference on Intelligent User Interfaces

Pages 603 - 613

https://doi.org/10.1145/3301275.3302311

Published: 17 March 2019 Publication History

Abstract

Career goals represent a special case for recommender systems and require considering both short and long term goals. Recommendations must represent a trade off between relevance to the user, achievability and aspirational goals to move the user forward in their career. Users may have different motivations and concerns when looking for a new long term goal, so involving the user in the recommender process becomes all the more important than in other domains. Additionally, the cost to the user of making a bad decision is much higher than investing two hours in watching a movie they don't like or listening to an unappealing song. As a result, we feel career recommendations is a unique opportunity to truly engage the user in an interactive recommender as we believe they will invest the cognitive load. In this paper, we present an interactive career goal recommender framework that leverages the power of dialogue to allow the user interactively improve the recommendations and bring their own preferences to the system. The underlying recommendation algorithm is a novel solution that suggests both short and long term goals through utilizing the sequential patterns extracted from career trajectories that are enhanced with features of the supporting user profiles. The effectiveness of the proposed solution is demonstrated with extensive experiments on two real world data sets.

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Cited By

Ma BYang TRen B(2024)A Survey on Explainable Course Recommendation SystemsDistributed, Ambient and Pervasive Interactions10.1007/978-3-031-60012-8_17(273-287)Online publication date: 1-Jun-2024
https://doi.org/10.1007/978-3-031-60012-8_17
Buschek DEiband MHussmann H(2022)How to Support Users in Understanding Intelligent Systems? An Analysis and Conceptual Framework of User Questions Considering User Mindsets, Involvement, and Knowledge OutcomesACM Transactions on Interactive Intelligent Systems10.1145/351926412:4(1-27)Online publication date: 27-Apr-2022
https://dl.acm.org/doi/10.1145/3519264
Ma BLu MTaniguchi YKonomi S(2021)CourseQ: the impact of visual and interactive course recommendation in university environmentsResearch and Practice in Technology Enhanced Learning10.1186/s41039-021-00167-716:1Online publication date: 30-Jun-2021
https://doi.org/10.1186/s41039-021-00167-7
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Index Terms

Where can my career take me?: harnessing dialogue for interactive career goal recommendations
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods

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cover image ACM Conferences

IUI '19: Proceedings of the 24th International Conference on Intelligent User Interfaces

March 2019

713 pages

ISBN:9781450362726

DOI:10.1145/3301275

General Chairs:
Wai-Tat Fu,
Shimei Pan,
Program Chairs:
Oliver Brdiczka,
Polo Chau,
Gaelle Calvary

Copyright © 2019 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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Published: 17 March 2019

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IUI '19: 24th International Conference on Intelligent User Interfaces

March 17 - 20, 2019

California, Marina del Ray

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IUI '19 Paper Acceptance Rate 71 of 282 submissions, 25%;

Overall Acceptance Rate 746 of 2,811 submissions, 27%

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Cited By

Ma BYang TRen B(2024)A Survey on Explainable Course Recommendation SystemsDistributed, Ambient and Pervasive Interactions10.1007/978-3-031-60012-8_17(273-287)Online publication date: 1-Jun-2024
https://doi.org/10.1007/978-3-031-60012-8_17
Buschek DEiband MHussmann H(2022)How to Support Users in Understanding Intelligent Systems? An Analysis and Conceptual Framework of User Questions Considering User Mindsets, Involvement, and Knowledge OutcomesACM Transactions on Interactive Intelligent Systems10.1145/351926412:4(1-27)Online publication date: 27-Apr-2022
https://dl.acm.org/doi/10.1145/3519264
Ma BLu MTaniguchi YKonomi S(2021)CourseQ: the impact of visual and interactive course recommendation in university environmentsResearch and Practice in Technology Enhanced Learning10.1186/s41039-021-00167-716:1Online publication date: 30-Jun-2021
https://doi.org/10.1186/s41039-021-00167-7
Herrmanny KTorkamaan H(2021)Towards a User Integration Framework for Personal Health Decision Support and Recommender SystemsProceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3450613.3456816(65-76)Online publication date: 21-Jun-2021
https://dl.acm.org/doi/10.1145/3450613.3456816
Eiband MBuschek DHussmann HHammond TVerbert KParra DKnijnenburg BO'Donovan JTeale P(2021)How to Support Users in Understanding Intelligent Systems? Structuring the DiscussionProceedings of the 26th International Conference on Intelligent User Interfaces10.1145/3397481.3450694(120-132)Online publication date: 14-Apr-2021
https://dl.acm.org/doi/10.1145/3397481.3450694
Gutiérrez FCharleer SDe Croon RHtun NGoetschalckx GVerbert KBogers TSaid ABrusilovsky PTikk D(2019)Explaining and exploring job recommendationsProceedings of the 13th ACM Conference on Recommender Systems10.1145/3298689.3347001(60-68)Online publication date: 10-Sep-2019
https://dl.acm.org/doi/10.1145/3298689.3347001

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