LSTM-based predictions for proactive information retrieval

Petri Luukkonen, Markus Koskela, Patrik Floreen

Research output: Chapter in Book/Report/Conference proceedingConference contributionProfessional

Abstract

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.
Original languageEnglish
Title of host publicationNeu-IR: The SIGIR 2016 Workshop on Neural Information Retrieval
Publication statusPublished - 21 Jul 2016
MoE publication typeD3 Professional conference proceedings
EventNeu-IR Workshop on Neural Information Retrieval - Pisa, Italy
Duration: 21 Jul 201621 Jul 2016
Conference number: 1

Workshop

WorkshopNeu-IR Workshop on Neural Information Retrieval
Country/TerritoryItaly
CityPisa
Period21/07/201621/07/2016

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