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

Published:09 July 2017Publication 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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  1. Encoding User as More Than the Sum of Their Parts: Recurrent Neural Networks and Word Embedding for People-to-people Recommendation

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      • 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,
        • Eelco Herder,
        • Program Chairs:
        • Federica Cena,
        • Michel Desmarais

        Copyright © 2017 ACM

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 9 July 2017

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        UMAP '17 Paper Acceptance Rate29of80submissions,36%Overall Acceptance Rate162of633submissions,26%

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