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.
@article{arxiv.2106.04376,
title = {ConSTR: A Contextual Search Term Recommender},
author = {Thomas Krämer and Zeljko Carevic and Dwaipayan Roy and Claus-Peter Klas and Philipp Mayr},
journal= {arXiv preprint arXiv:2106.04376},
year = {2021}
}
Comments
2 pages, 2 figures, accepted demo paper at JCDL 2021