research-article

Relational social recommendation: : Application to the academic domain

Authors:

Peter Brusilovsky,

Einat MinkovAuthors Info & Claims

Volume 124, Issue C

Pages 182 - 195

https://doi.org/10.1016/j.eswa.2019.01.061

Published: 15 June 2019 Publication History

Highlights

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Relational social recommendation provides highly detailed explanations to users.

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Users find these explanations to be intriguing and often surprising.

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Linking people based on entity associations reveals novel connections of interest.

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Recommendation relevance and serendipity may be improved given user feedbacks.

Abstract

This paper outlines RSR, a relational social recommendation approach applied to a social graph comprised of relational entity profiles. RSR uses information extraction and learning methods to obtain relational facts about persons of interest from the Web, and generates an associative entity-relation social network from their extracted personal profiles. As a case study, we consider the task of peer recommendation at scientific conferences. Given a social graph of scholars, RSR employs graph similarity measures to rank conference participants by their relatedness to a user. Unlike other recommender systems that perform social rankings, RSR provides the user with detailed supporting explanations in the form of relational connecting paths. In a set of user studies, we collected feedbacks from participants onsite of scientific conferences, pertaining to RSR quality of recommendations and explanations. The feedbacks indicate that users appreciate and benefit from RSR explainability features. The feedbacks further indicate on recommendation serendipity using RSR, having it recommend persons of interest who are not apriori known to the user, oftentimes exposing surprising inter-personal associations. Finally, we outline and assess potential gains in recommendation relevance and serendipity using path-based relational learning within RSR.

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Cited By

Mauro NHu ZArdissono L(2023)Justification of recommender systems results: a service-based approachUser Modeling and User-Adapted Interaction10.1007/s11257-022-09345-833:3(643-685)Online publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1007/s11257-022-09345-8
Hu ZBellogin ABoratto LSantos OArdissono LKnijnenburg B(2022)Service-aware Recommendation and Justification of ResultsProceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3503252.3534357(341-345)Online publication date: 4-Jul-2022
https://dl.acm.org/doi/10.1145/3503252.3534357
Fareri SMelluso NChiarello FFantoni G(2021)SkillNERExpert Systems with Applications: An International Journal10.1016/j.eswa.2021.115544184:COnline publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1016/j.eswa.2021.115544

Index Terms

Relational social recommendation: Application to the academic domain
1. Human-centered computing
2. Information systems

Index terms have been assigned to the content through auto-classification.

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cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 124, Issue C

Jun 2019

349 pages

ISSN:0957-4174

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Elsevier Ltd.

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Pergamon Press, Inc.

United States

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Published: 15 June 2019

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Cited By

Mauro NHu ZArdissono L(2023)Justification of recommender systems results: a service-based approachUser Modeling and User-Adapted Interaction10.1007/s11257-022-09345-833:3(643-685)Online publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1007/s11257-022-09345-8
Hu ZBellogin ABoratto LSantos OArdissono LKnijnenburg B(2022)Service-aware Recommendation and Justification of ResultsProceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3503252.3534357(341-345)Online publication date: 4-Jul-2022
https://dl.acm.org/doi/10.1145/3503252.3534357
Fareri SMelluso NChiarello FFantoni G(2021)SkillNERExpert Systems with Applications: An International Journal10.1016/j.eswa.2021.115544184:COnline publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1016/j.eswa.2021.115544

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