research-article

Building recommender systems for scholarly information

Authors:

Maya Hristakeva,

Daniel Kershaw,

Marco Rossetti,

Benjamin Pettit,

Kris JackAuthors Info & Claims

SWM '17: Proceedings of the 1st Workshop on Scholarly Web Mining

Pages 25 - 32

https://doi.org/10.1145/3057148.3057152

Published: 10 February 2017 Publication History

Abstract

The depth and breadth of research now being published is overwhelming for an individual researcher to keep track of let alone consume. Recommender systems have been developed to make it easier for researchers to discover relevant content. However, these have predominately taken the form of item-to-item recommendations using citation network features or text similarity features.

This paper details how the Mendeley Suggest recommender system has been designed and developed. We show how implicit user feedback (based on activity data from the reference manager) and collaborative filtering (CF) are used to generate the recommendations for Mendeley Suggest. Because collaborative filtering suffers from the cold start problem (the inability to serve recommendations to new users), we developed additional recommendation methods based on user-defined attributes, such as discipline and research interests.

Our off-line evaluation shows that where possible, recommendations based on collaborative filtering perform best, followed by recommendations based on recent activity. However, for cold users (for whom collaborative filtering was not possible) recommendations based on discipline performed best. Additionally, when we segmented users by career stages, we found that among senior academics, content-based recommendations from recent activity had comparable performance to collaborative filtering. This justifies our approach of developing a variety of recommendation methods, in order to serve a range of users across the academic spectrum.

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Cited By

Wang GZhou LGong JZhang X(2024)Heterogeneous Graph Neural Network with Hierarchical Attention for Group-Aware Paper Recommendation in Scientific Social NetworksApplied Soft Computing10.1016/j.asoc.2024.112448(112448)Online publication date: Nov-2024
https://doi.org/10.1016/j.asoc.2024.112448
Verma SHarit SMunjal K(2024)SRRS: Design and Development of a Scholarly Reciprocal Recommendation SystemScientometrics10.1007/s11192-024-05143-8129:11(6839-6866)Online publication date: 21-Sep-2024
https://doi.org/10.1007/s11192-024-05143-8
Chen WZhang YXian YWen Y(2023)Hotspot Information Network and Domain Knowledge Graph Aggregation in Heterogeneous Network for Literature RecommendationApplied Sciences10.3390/app1302109313:2(1093)Online publication date: 13-Jan-2023
https://doi.org/10.3390/app13021093
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cover image ACM Other conferences

SWM '17: Proceedings of the 1st Workshop on Scholarly Web Mining

February 2017

65 pages

ISBN:9781450352406

DOI:10.1145/3057148

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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Oak Ridge National Laboratory
OU: The Open University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 February 2017

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SWM '17

SWM '17: 1st Workshop on Scholarly Web Mining

February 10, 2017

Cambridge, United Kingdom

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SWM '17 Paper Acceptance Rate 8 of 17 submissions, 47%;

Overall Acceptance Rate 8 of 17 submissions, 47%

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Cited By

Wang GZhou LGong JZhang X(2024)Heterogeneous Graph Neural Network with Hierarchical Attention for Group-Aware Paper Recommendation in Scientific Social NetworksApplied Soft Computing10.1016/j.asoc.2024.112448(112448)Online publication date: Nov-2024
https://doi.org/10.1016/j.asoc.2024.112448
Verma SHarit SMunjal K(2024)SRRS: Design and Development of a Scholarly Reciprocal Recommendation SystemScientometrics10.1007/s11192-024-05143-8129:11(6839-6866)Online publication date: 21-Sep-2024
https://doi.org/10.1007/s11192-024-05143-8
Chen WZhang YXian YWen Y(2023)Hotspot Information Network and Domain Knowledge Graph Aggregation in Heterogeneous Network for Literature RecommendationApplied Sciences10.3390/app1302109313:2(1093)Online publication date: 13-Jan-2023
https://doi.org/10.3390/app13021093
Sharma RGopalani DMeena Y(2023)An anatomization of research paper recommender systemEngineering Applications of Artificial Intelligence10.1016/j.engappai.2022.105641118:COnline publication date: 1-Feb-2023
https://dl.acm.org/doi/10.1016/j.engappai.2022.105641
Zhang ZPatra BYaseen AZhu JSabharwal RRoberts KCao TWu H(2023)Scholarly recommendation systems: a literature surveyKnowledge and Information Systems10.1007/s10115-023-01901-x65:11(4433-4478)Online publication date: 4-Jun-2023
https://doi.org/10.1007/s10115-023-01901-x
Perez Maurera FFerrari Dacrema MCremonesi P(2022)Towards the Evaluation of Recommender Systems with ImpressionsProceedings of the 16th ACM Conference on Recommender Systems10.1145/3523227.3551483(610-615)Online publication date: 12-Sep-2022
https://dl.acm.org/doi/10.1145/3523227.3551483
Hidayat DSensuse DElisabeth DHasani L(2022)Conceptual model of knowledge management system for scholarly publication cycle in academic institutionVINE Journal of Information and Knowledge Management Systems10.1108/VJIKMS-08-2021-0163Online publication date: 8-Dec-2022
https://doi.org/10.1108/VJIKMS-08-2021-0163
Lee DStvilia BHa SHahn D(2022)The structure and priorities of researchers' scholarly profile maintenance activities: A case of institutional research information management systemJournal of the Association for Information Science and Technology10.1002/asi.2472174:2(186-204)Online publication date: 8-Nov-2022
https://doi.org/10.1002/asi.24721
KOBAYASHI KKAZAMA KYOSHIDA MOHMUKAI ISATO S(2020)Generality Analysis Method of Research Papers Using User Behavior Data on the Internetインターネット上のユーザの行動データを用いた論文の普遍性の分析手法Joho Chishiki Gakkaishi10.2964/jsik_2020_007Online publication date: 2020
https://doi.org/10.2964/jsik_2020_007
Khadka ACantador IFernandez M(2020)Capturing and Exploiting Citation Knowledge for Recommending Recently Published Papers2020 IEEE 29th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)10.1109/WETICE49692.2020.00054(239-244)Online publication date: Sep-2020
https://doi.org/10.1109/WETICE49692.2020.00054
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