Evaluating Task-Dependent Taxonomies for Navigation
| Autor: | Yuyin Sun, Adish Singla, Tori Yan, Andreas Krause, Dieter Fox |
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| Rok vydání: | 2016 |
| Zdroj: | Proceedings of the AAAI Conference on Human Computation and Crowdsourcing. 4:229-238 |
| ISSN: | 2769-1349 2769-1330 |
| DOI: | 10.1609/hcomp.v4i1.13286 |
| Popis: | Taxonomies of concepts are important across many application domains, for instance, online shopping portals use catalogs to help users navigate and search for products. Task-dependent taxonomies, e.g., adapting the taxonomy to a specific cohort of users, can greatly improve the effectiveness of navigation and search. However, taxonomies are usually created by domain experts and hence designing task-dependent taxonomies can be an expensive process: this often limits the applications to deploy generic taxonomies. Crowdsourcing-based techniques have the potential to provide a cost-efficient solution to building task-dependent taxonomies. In this paper, we present the first quantitative study to evaluate the effectiveness of these crowdsourcing based techniques. Our experimental study compares different task-dependent taxonomies built via crowdsourcing and generic taxonomies built by experts. We design randomized behavioral experiments on the Amazon Mechanical Turk platform for navigation tasks using these taxonomies resembling real-world applications such as product search. We record various metrics such as the time of navigation, the number of clicks performed, and the search path taken by a participant to navigate the taxonomy to locate a desired object. Our findings show that task-dependent taxonomies built by crowdsourcing techniques can reduce the navigation time up to $20\%$. Our results, in turn,demonstrate the power of crowdsourcing for learning complex structures such as semantic taxonomies. |
| Databáze: | OpenAIRE |
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