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Venue Topic Model–enhanced Joint Graph Modelling for Citation Recommendation in Scholarly Big Data

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Published:01 December 2020Publication History
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Editorial Notes

The editors have requested minor, non-substantive changes to the VoR and, in accordance with ACM policies, a Corrected VoR was published on February 9, 2021. For reference purposes the VoR may still be accessed via the Supplemental Material section on this page.

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Abstract

Natural language processing technologies, such as topic models, have been proven to be effective for scholarly recommendation tasks with the ability to deal with content information. Recently, venue recommendation is becoming an increasingly important research task due to the unprecedented number of publication venues. However, traditional methods focus on either the author’s local network or author-venue similarity, where the multiple relationships between scholars and venues are overlooked, especially the venue–venue interaction. To solve this problem, we propose an author topic model–enhanced joint graph modeling approach that consists of venue topic modeling, venue-specific topic influence modeling, and scholar preference modeling. We first model the venue topic with Latent Dirichlet Allocation. Then, we model the venue-specific topic influence in an asymmetric and low-dimensional way by considering the topic similarity between venues, the top-influence of venues, and the top-susceptibility of venues. The top-influence characterizes venues’ capacity of exerting topic influence on other venues. The top-susceptibility captures venues’ propensity of being topically influenced by other venues. Extensive experiments on two real-world datasets show that our proposed joint graph modeling approach outperforms the state-of-the-art methods.

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      • Published in

        cover image ACM Transactions on Asian and Low-Resource Language Information Processing
        ACM Transactions on Asian and Low-Resource Language Information Processing  Volume 20, Issue 1
        Special issue on Deep Learning for Low-Resource Natural Language Processing, Part 1 and Regular Papers
        January 2021
        332 pages
        ISSN:2375-4699
        EISSN:2375-4702
        DOI:10.1145/3439335
        Issue’s Table of Contents

        Copyright © 2020 ACM

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 1 December 2020
        • Accepted: 1 June 2020
        • Revised: 1 May 2020
        • Received: 1 April 2020
        Published in tallip Volume 20, Issue 1

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