research-article

Assessing the Contribution of Twitter's Textual Information to Graph-based Recommendation

Authors:

Evgenia Wasserman Pritsker,

Einat MinkovAuthors Info & Claims

IUI '17: Proceedings of the 22nd International Conference on Intelligent User Interfaces

Pages 511 - 516

https://doi.org/10.1145/3025171.3025218

Published: 07 March 2017 Publication History

Abstract

Graph-based recommendation approaches can model associations between users and items alongside additional contextual information. Recent studies demonstrated that representing features extracted from social media (SM) auxiliary data, like friendships, jointly with traditional users/items ratings in the graph, contribute to recommendation accuracy. In this work, we take a step further and propose an extended graph representation that includes socio-demographic and personal traits extracted from the content posted by the user on SM. Empirical results demonstrate that processing unstructured textual information collected from Twitter and representing it in structured form in the graph improves recommendation performance, especially in cold start conditions.

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Cited By

Lotan NMinkov E(2023)Social world knowledge: Modeling and applicationsPLOS ONE10.1371/journal.pone.028370018:7(e0283700)Online publication date: 7-Jul-2023
https://doi.org/10.1371/journal.pone.0283700
Kulkarni APowell LMurphy SRao NChu S(2023)Everyday-Inspired Movies: Towards the Design of Movie Recommender Systems based on Everyday Life through Personal Social MediaHuman-Computer Interaction – INTERACT 202310.1007/978-3-031-42286-7_9(160-169)Online publication date: 25-Aug-2023
https://doi.org/10.1007/978-3-031-42286-7_9
Alhijawi BAL-Naymat G(2022)Novel Positive Multi-Layer Graph Based Method for Collaborative Filtering Recommender SystemsJournal of Computer Science and Technology10.1007/s11390-021-0420-237:4(975-990)Online publication date: 30-Jul-2022
https://doi.org/10.1007/s11390-021-0420-2
Show More Cited By

Index Terms

Assessing the Contribution of Twitter's Textual Information to Graph-based Recommendation
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
2. Information systems
  1. Information systems applications

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Published In

cover image ACM Conferences

IUI '17: Proceedings of the 22nd International Conference on Intelligent User Interfaces

March 2017

654 pages

ISBN:9781450343480

DOI:10.1145/3025171

General Chairs:
George A. Papadopoulos
The University of Cyprus, Cyprus
,
Tsvi Kuflik
The University of Haifa, Israel
,
Program Chairs:
Fang Chen
Data61, CSIRO Australia
,
Carlos Duarte
The University of Lisbon, Portugal
,
Wai-Tat Fu
The University of Illinois at Urbana-Champaign, USA

Copyright © 2017 ACM.

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Published: 07 March 2017

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Magnet InfoMedia
Israeli Science Foundation

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IUI'17

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IUI'17: 22nd International Conference on Intelligent User Interfaces

March 13 - 16, 2017

Limassol, Cyprus

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IUI '17 Paper Acceptance Rate 63 of 272 submissions, 23%;

Overall Acceptance Rate 746 of 2,811 submissions, 27%

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Cited By

Lotan NMinkov E(2023)Social world knowledge: Modeling and applicationsPLOS ONE10.1371/journal.pone.028370018:7(e0283700)Online publication date: 7-Jul-2023
https://doi.org/10.1371/journal.pone.0283700
Kulkarni APowell LMurphy SRao NChu S(2023)Everyday-Inspired Movies: Towards the Design of Movie Recommender Systems based on Everyday Life through Personal Social MediaHuman-Computer Interaction – INTERACT 202310.1007/978-3-031-42286-7_9(160-169)Online publication date: 25-Aug-2023
https://doi.org/10.1007/978-3-031-42286-7_9
Alhijawi BAL-Naymat G(2022)Novel Positive Multi-Layer Graph Based Method for Collaborative Filtering Recommender SystemsJournal of Computer Science and Technology10.1007/s11390-021-0420-237:4(975-990)Online publication date: 30-Jul-2022
https://doi.org/10.1007/s11390-021-0420-2
Suhaim ABerri J(2021)Context-Aware Recommender Systems for Social Networks: Review, Challenges and OpportunitiesIEEE Access10.1109/ACCESS.2021.30721659(57440-57463)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3072165
(2018)Recommendation research trendsInternational Journal of Web Engineering and Technology10.1504/IJWET.2018.09283113:2(123-186)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1504/IJWET.2018.092831

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