ConSTR: A Contextual Search Term Recommender
Published at : October 02, 2021
Poster pitch by Thomas Krämer at the ACM/IEEE Joint Conference on Digital Libraries 2021 (virtual conference).
Demo paper: http://arxiv.org/abs/2106.04376
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.
Demo paper: http://arxiv.org/abs/2106.04376
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.
contextual retrievalsearch term recommendationarxiv