Query Log Mining for Inferring User Tasks and Needs
Abstract
Search behavior, and information seeking behavior more generally, is often motivated by tasks that prompt search processes that are often lengthy, iterative, and intermittent, and are characterized by distinct stages, shifting goals and multitasking. Current search systems do not provide adequate support for users tackling complex tasks due to which the cognitive burden of keeping track of such tasks is placed on the searcher. In this note, we summarize our recent efforts towards extracting search tasks from search logs. Based on recent advancements in Bayesian Nonparametrics and distributional semantics, we propose novel algorithms to extract task and subtasks from a query collection. The models discussed can inform the design of the next generation of task-based search systems that leverage user’s task behavior for better support and personalization.
Cite
Text
Mehrotra and Yilmaz. "Query Log Mining for Inferring User Tasks and Needs." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016. doi:10.1007/978-3-319-46131-1_36Markdown
[Mehrotra and Yilmaz. "Query Log Mining for Inferring User Tasks and Needs." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016.](https://mlanthology.org/ecmlpkdd/2016/mehrotra2016ecmlpkdd-query/) doi:10.1007/978-3-319-46131-1_36BibTeX
@inproceedings{mehrotra2016ecmlpkdd-query,
title = {{Query Log Mining for Inferring User Tasks and Needs}},
author = {Mehrotra, Rishabh and Yilmaz, Emine},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2016},
pages = {284-288},
doi = {10.1007/978-3-319-46131-1_36},
url = {https://mlanthology.org/ecmlpkdd/2016/mehrotra2016ecmlpkdd-query/}
}