research-article

ConSTR: A Contextual Search Term Recommender

Authors:

Philipp MayrAuthors Info & Claims

JCDL '21: Proceedings of the 2021 ACM/IEEE Joint Conference on Digital Libraries

Pages 295 - 296

https://doi.org/10.1109/JCDL52503.2021.00042

Published: 13 September 2024 Publication History

Get Access

Abstract

In this demo paper, we present ConSTR, a novel Contextual Search Term Recommender that utilises the user's interaction context for search term recommendation and literature retrieval. ConSTR integrates a two-layered recommendation interface: the first layer suggests terms with respect to a user's current search term, and the second layer suggests terms based on the users' previous search activities (interaction context). For the demonstration, ConSTR is built on the arXiv, an academic repository consisting of 1.8 million documents.

References

[1]

G. Marchionini, "Exploratory search: from finding to understanding," Communications of the ACM, vol. 49, no. 4, pp. 41--46, 2006.

Digital Library

Google Scholar

[2]

C. C. Kuhlthau, "A principle of uncertainty for information seeking," Journal of documentation, 1993.

Crossref

Google Scholar

[3]

R. W. White and R. A. Roth, "Exploratory search: Beyond the query-response paradigm," Synthesis lectures on information concepts, retrieval, and services, vol. 1, no. 1, pp. 1--98, 2009.

Google Scholar

[4]

P. Mutschke, P. Mayr, P. Schaer, and Y. Sure, "Science models as value-added services for scholarly information systems," Scientometrics, vol. 89, no. 1, pp. 349--364, 2011.

Digital Library

Google Scholar

[5]

N. Fuhr, C.-P. Klas, A. Schaefer, and P. Mutschke, "Daffodil: An integrated desktop for supporting high-level search activities in federated digital libraries," in Research and Advanced Technology for Digital Libraries, M. Agosti and C. Thanos, Eds., 2002, pp. 597--612.

Google Scholar

[6]

Z. Carevic, S. Schüller, P. Mayr, and N. Fuhr, "Contextualised browsing in a digital library's living lab," in Proceedings of the 18th ACM/IEEE on Joint Conference on Digital Libraries, 2018, p. 89--98.

Google Scholar

[7]

D. Roy, D. Ganguly, S. Bhatia, S. Bedathur, and M. Mitra, "Using word embeddings for information retrieval: How collection and term normalization choices affect performance," in Proc. of 27th ACM CIKM 2018, Italy, October 22--26, 2018. ACM, 2018, pp. 1835--1838.

Google Scholar

[8]

R. Campos, V. Mangaravite, A. Pasquali, A. M. Jorge, C. Nunes, and A. Jatowt, "Yake! collection-independent automatic keyword extractor," in ECIR. Springer, 2018, pp. 806--810.

Google Scholar

Index Terms

ConSTR: A Contextual Search Term Recommender
1. Information systems
  1. Information retrieval

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

Recommendations

Exploiting contextual information in recommender systems
RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems

Recommender Systems help an on-line user to tame information overload and are being used now in complex domains where it could be beneficial to exploit context-awareness, e.g., in travel recommendation. Technically, in Recommender Systems we can ...
A Contextual Bayesian User Experience Model for Scholarly Recommender Systems
Artificial Intelligence in HCI
Abstract
Since the advent of scholarly recommender systems (SRSs), more than 200 papers in the related area have been published. Many of these papers focus on proposing new and more accurate algorithms, or to enhance existing ones. Recently we have seen ...
Augmenting service recommender systems by incorporating contextual opinions from user reviews

Context-aware recommender systems have been widely investigated in both academia and industry because they can make recommendations based on a user's current context (e.g., location, time). However, most existing context-aware techniques only use ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

JCDL '21: Proceedings of the 2021 ACM/IEEE Joint Conference on Digital Libraries

September 2021

385 pages

ISBN:9781665417709

DOI:10.1109/3686225

General Chair:
J. Stephen Downie
University of Illinois at Urbana-Champaign, USA

Publisher

IEEE Press

Publication History

Published: 13 September 2024

Check for updates

Author Tags

Qualifiers

Research-article

Conference

JCDL '21

Sponsor:

JCDL '21: 2021 ACM/IEEE Joint Conference on Digital Libraries

September 27 - 30, 2021

, Virtual Event

Acceptance Rates

Overall Acceptance Rate 415 of 1,482 submissions, 28%

Upcoming Conference

JCDL '24

Sponsor:
sigir
sigir

The 2024 ACM/IEEE Joint Conference on Digital Libraries

December 16 - 20, 2024

Hong Kong , China

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

0
Total Citations
2
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)1

Reflects downloads up to 21 Nov 2024

Other Metrics

View Author Metrics

Citations

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Index Terms

Recommendations

Exploiting contextual information in recommender systems

A Contextual Bayesian User Experience Model for Scholarly Recommender Systems

Augmenting service recommender systems by incorporating contextual opinions from user reviews