ConSTR: A Contextual Search Term Recommender
This is an incremental improvement for users of academic repositories like arXiv.
The paper tackles the problem of search term recommendation by introducing ConSTR, a contextual recommender that uses user interaction context, resulting in a system built on a dataset of 1.8 million arXiv documents.
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.