ConSTR: A Contextual Search Term Recommender
2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), 2021•ieeexplore.ieee.org
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 …
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 …
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.
ieeexplore.ieee.org
