research-article

QuoteRec: Toward Quote Recommendation for Writing

Authors:

Jianguo XiaoAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 36, Issue 3

Article No.: 34, Pages 1 - 36

https://doi.org/10.1145/3183370

Published: 23 March 2018 Publication History

Abstract

Quote is a language phenomenon of transcribing the statement of someone else, such as a proverb and a famous saying. An appropriate usage of quote usually equips the expression with more elegance and credibility. However, there are times when we are eager to stress our idea by citing a quote, while nothing relevant comes to mind. Therefore, it is exciting to have a recommender system which provides quote recommendations while we are writing. This article extends previous study of quote recommendation, the task that recommends the appropriate quote according to the context (i.e., the content occurring before and after the quote). In this article, a quote recommender system called QuoteRec is presented to tackle the task. We investigate two models to learn the vector representations of quotes and contexts, and then rank the candidate quotes based on the representations. The first model learns the quote representation according to the contexts of a quote. The second model is an extension of the neural network model in previous study, which learns the representation of a quote by concerning both its content and contexts. Experimental results demonstrate the effectiveness of the two models in learning the semantic representations of quotes, and the neural network model achieves state-of-the-art results on the quote recommendation task.

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Index Terms

QuoteRec: Toward Quote Recommendation for Writing
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Published In

cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 36, Issue 3

July 2018

402 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3146384

Editor:
Maarten de Rijke
University of Amsterdam, The Netherlands

Issue’s Table of Contents

Copyright © 2018 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 ACM 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 March 2018

Accepted: 01 January 2018

Revised: 01 January 2018

Received: 01 November 2016

Published in TOIS Volume 36, Issue 3

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National Natural Science Foundation of China
863 Program of China

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Vuong TAndolina SJacucci GRuotsalo T(2021)Does More Context Help? Effects of Context Window and Application Source on Retrieval PerformanceACM Transactions on Information Systems10.1145/347405540:2(1-40)Online publication date: 27-Sep-2021
https://dl.acm.org/doi/10.1145/3474055
Jia YDou HCao SZan H(2021)Speaker Identification and Its Application to Social Network Construction for Chinese NovelsInternational Journal of Asian Language Processing10.1142/S271755452050018630:04(2050018)Online publication date: 8-May-2021
https://doi.org/10.1142/S2717554520500186
Chavare SAwati CShirgave S(2021)Smart Recommender System using Deep Learning2021 6th International Conference on Inventive Computation Technologies (ICICT)10.1109/ICICT50816.2021.9358580(590-594)Online publication date: 20-Jan-2021
https://doi.org/10.1109/ICICT50816.2021.9358580
Jia YDou HCao SZan H(2020)Speaker Identification and Its Application to Social Network Construction for Chinese Novels2020 International Conference on Asian Language Processing (IALP)10.1109/IALP51396.2020.9310515(13-18)Online publication date: 4-Dec-2020
https://doi.org/10.1109/IALP51396.2020.9310515
Jeong CJang SPark EChoi S(2020)A context-aware citation recommendation model with BERT and graph convolutional networksScientometrics10.1007/s11192-020-03561-y124:3(1907-1922)Online publication date: 13-Jul-2020
https://dl.acm.org/doi/10.1007/s11192-020-03561-y
Sreenu GSaleem Durai M(2019)Intelligent video surveillance: a review through deep learning techniques for crowd analysisJournal of Big Data10.1186/s40537-019-0212-56:1Online publication date: 6-Jun-2019
https://doi.org/10.1186/s40537-019-0212-5
Lv PMeng XZhang Y(2019)BoReACM Transactions on Information Systems10.1145/336121738:1(1-33)Online publication date: 17-Oct-2019
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Guan XCheng ZHe XZhang YZhu ZPeng QChua T(2019)Attentive Aspect Modeling for Review-Aware RecommendationACM Transactions on Information Systems10.1145/330954637:3(1-27)Online publication date: 27-Mar-2019
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Xiao RJi JCui BTang HOu WXiao YTan JJu XCulpepper JMoffat ABennett PLerman K(2019)Weakly Supervised Co-Training of Query Rewriting andSemantic Matching for e-CommerceProceedings of the Twelfth ACM International Conference on Web Search and Data Mining10.1145/3289600.3291039(402-410)Online publication date: 30-Jan-2019
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