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Extracting Hierarchies of Search Tasks & Subtasks via a Bayesian Nonparametric Approach

Mehrotra, R; Yilmaz, E; (2017) Extracting Hierarchies of Search Tasks & Subtasks via a Bayesian Nonparametric Approach. In: (Proceedings) SIGIR '17: 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. (pp. pp. 285-294). ACM: Shinjuku, Tokyo; Japan. Green open access

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Abstract

A significant amount of search queries originate from some real world information need or tasks [13]. In order to improve the search experience of the end users, it is important to have accurate representations of tasks. As a result, significant amount of research has been devoted to extracting proper representations of tasks in order to enable search systems to help users complete their tasks, as well as providing the end user with better query suggestions [9], for better recommendations [41], for satisfaction prediction [36] and for improved personalization in terms of tasks [24, 38]. Most existing task extraction methodologies focus on representing tasks as flat structures. However, tasks often tend to have multiple subtasks associated with them and a more naturalistic representation of tasks would be in terms of a hierarchy, where each task can be composed of multiple (sub)tasks. To this end, we propose an efficient Bayesian nonparametric model for extracting hierarchies of such tasks & subtasks. We evaluate our method based on real world query log data both through quantitative and crowdsourced experiments and highlight the importance of considering task/subtask hierarchies.

Type: Proceedings paper
Title: Extracting Hierarchies of Search Tasks & Subtasks via a Bayesian Nonparametric Approach
Event: SIGIR '17: 40th International ACM SIGIR Conference on Research and Development in Information Retrieval
Location: Shinjuku, Tokyo, Japan
Dates: 07 August 2017 - 11 August 2017
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/3077136.3080823
Publisher version: https://doi.org/10.1145/3077136.3080823
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: http://discovery.ucl.ac.uk/id/eprint/10070791
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