LSTM-Based Predictions for Proactive Information Retrieval

Autor:	Luukkonen, Petri, Koskela, Markus, Floréen, Patrik
Rok vydání:	2016
Předmět:	Computer Science - Information Retrieval Computer Science - Computation and Language Computer Science - Neural and Evolutionary Computing
Druh dokumentu:	Working Paper
Popis:	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. Comment: Neu-IR '16 SIGIR Workshop on Neural Information Retrieval, July 21, 2016, Pisa, Italy
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1606.06137 Zobrazit plný text záznamu View this record from Arxiv