LSTM-Based Predictions for Proactive Information Retrieval
This addresses the problem of improving information retrieval efficiency for writers, but it is incremental as it applies an existing LSTM method to a specific domain.
The paper tackles proactive information retrieval for writing tasks by predicting user needs and recommending relevant background information using an LSTM network, achieving higher precision in simulations compared to baselines.
We describe a method for proactive information retrieval targeted at retrieving relevant information during a writing task. In our method, the current task and the needs of the user are estimated, and the potential next steps are unobtrusively predicted based on the user's past actions. We focus on the task of writing, in which the user is coalescing previously collected information into a text. Our proactive system automatically recommends the user relevant background information. The proposed system incorporates text input prediction using a long short-term memory (LSTM) network. We present simulations, which show that the system is able to reach higher precision values in an exploratory search setting compared to both a baseline and a comparison system.