We address the problem of generating query suggestions to support users in completing their underlying tasks (which motivated them to search in the first place). Given an initial query, these query suggestions should provide a coverage of possible subtasks the user might be looking for. We propose a probabilistic modeling framework that obtains keyphrases from multiple sources and generates query suggestions from these keyphrases. Using the test suites of the TREC Tasks track, we evaluate and analyze each component of our model.
@article{arxiv.1708.08289,
title = {Generating Query Suggestions to Support Task-Based Search},
author = {Darío Garigliotti and Krisztian Balog},
journal= {arXiv preprint arXiv:1708.08289},
year = {2017}
}
Comments
Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '17), 2017