ConSTR: A Contextual Search Term Recommender

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...

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Bibliographic Details
Published in2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL) pp. 295 - 296
Main Authors Kramer, Thomas, Carevic, Zeljko, Roy, Dwaipayan, Klas, Claus-Peter, Mayr, Philipp
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2021
Subjects
Context modeling
Interaction Context
Libraries
Query Recommendation
User modelling
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Summary: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.
DOI:10.1109/JCDL52503.2021.00042