short-paper

Encoding User as More Than the Sum of Their Parts: Recurrent Neural Networks and Word Embedding for People-to-people Recommendation

Authors:

Antoine Lefebvre-Brossard,

Alexandre Spaeth,

Michel C. DesmaraisAuthors Info & Claims

UMAP '17: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization

Pages 298 - 302

https://doi.org/10.1145/3079628.3079700

Published: 09 July 2017 Publication History

Abstract

Neural networks and word embeddings are powerful tools to capture latent factors. These tools can provide effective measures of similarities between users or items in the context of sparse data. We propose a novel approach that relies on neural networks and word embeddings to the problem of matching a learner looking for mentoring, and a tutor that is willing to provide this mentoring. Tutors and learners can issue multiple offers/requests on different topics. The approach matches over the whole array of topics specified by learners and tutors. Its performance for tutor-learner matching is compared with the state of the art. It yields similar results in terms of precision, but improves the recall.

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Cited By

Pardos ZRosenbaum LAbrahamson D(2022)Characterizing learner behavior from touchscreen dataInternational Journal of Child-Computer Interaction10.1016/j.ijcci.2021.10035731:COnline publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1016/j.ijcci.2021.100357
Ding ZLi X(2021)College Students’ Portrait Technology Based on Hybrid Neural NetworkSpatial Data and Intelligence10.1007/978-3-030-69873-7_12(165-183)Online publication date: 28-Feb-2021
https://doi.org/10.1007/978-3-030-69873-7_12
Zhou YShang YCao YLiu YTan J(2019)UAFA: Unsupervised Attribute-Friendship Attention Framework for User RepresentationKnowledge Science, Engineering and Management10.1007/978-3-030-29551-6_14(155-167)Online publication date: 21-Aug-2019
https://doi.org/10.1007/978-3-030-29551-6_14
Show More Cited By

Index Terms

Encoding User as More Than the Sum of Their Parts: Recurrent Neural Networks and Word Embedding for People-to-people Recommendation
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Collaborative filtering

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Information & Contributors

Information

Published In

cover image ACM Conferences

UMAP '17: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization

July 2017

420 pages

ISBN:9781450346351

DOI:10.1145/3079628

General Chairs:
Maria Bielikova
Slovak University of Technology in Bratislava, Slovakia
,
Eelco Herder
L3S Research Center, Germany and Radboud Universiteit Nijmegen, The Netherlands
,
Program Chairs:
Federica Cena
University of Torino, Italy
,
Michel Desmarais
Polytechnique University, Montreal, Canada

Copyright © 2017 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 the author(s) 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

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Published: 09 July 2017

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Short-paper

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Mitacs

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UMAP '17

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UMAP '17: 25th Conference on User Modeling, Adaptation and Personalization

July 9 - 12, 2017

Bratislava, Slovakia

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UMAP '17 Paper Acceptance Rate 29 of 80 submissions, 36%;

Overall Acceptance Rate 162 of 633 submissions, 26%

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UMAP '25

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33rd ACM Conference on User Modeling, Adaptation and Personalization

June 16 - 19, 2025

New York City , NY , USA

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Cited By

Pardos ZRosenbaum LAbrahamson D(2022)Characterizing learner behavior from touchscreen dataInternational Journal of Child-Computer Interaction10.1016/j.ijcci.2021.10035731:COnline publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1016/j.ijcci.2021.100357
Ding ZLi X(2021)College Students’ Portrait Technology Based on Hybrid Neural NetworkSpatial Data and Intelligence10.1007/978-3-030-69873-7_12(165-183)Online publication date: 28-Feb-2021
https://doi.org/10.1007/978-3-030-69873-7_12
Zhou YShang YCao YLiu YTan J(2019)UAFA: Unsupervised Attribute-Friendship Attention Framework for User RepresentationKnowledge Science, Engineering and Management10.1007/978-3-030-29551-6_14(155-167)Online publication date: 21-Aug-2019
https://doi.org/10.1007/978-3-030-29551-6_14
Pardos ZHu CMeng PNeff MAbrahamson DMitrovic TZhang JChen LChin D(2018)Classifying Learner Behavior from High Frequency Touchscreen Data Using Recurrent Neural NetworksAdjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization10.1145/3213586.3225244(317-322)Online publication date: 2-Jul-2018
https://dl.acm.org/doi/10.1145/3213586.3225244

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