ACM Computing Surveys (CSUR)
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
Automatic construction of multifaceted browsing interfaces
Proceedings of the 14th ACM international conference on Information and knowledge management
Decision trees for entity identification: approximation algorithms and hardness results
Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Minimum-effort driven dynamic faceted search in structured databases
Proceedings of the 17th ACM conference on Information and knowledge management
DynaCet: Building Dynamic Faceted Search Systems over Databases
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Reducing working memory load in spoken dialogue systems
Interacting with Computers
Proceedings of the 2010 International Cross Disciplinary Conference on Web Accessibility (W4A)
Faceted search and browsing of audio content on spoken web
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
A comparative user study of faceted search in large data hierarchies on mobile devices
Proceedings of the 12th International Conference on Mobile and Ubiquitous Multimedia
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Designing interactive voice systems that have optimum cognitive load on callers has been an active research topic for quite some time. There have been many studies comparing the user preferences on navigation trees with higher depths over higher breadths. In this paper, we consider the navigation of structured data containing various types of attributes using phone-based interactions. This problem is particularly relevant to emerging economies in which innovative voice-based applications are being built to address semi-literate population. We address the problem of identifying the right sequence of facets to be presented to the user for phone-based navigation of the data in two stages. Firstly, we perform extensive user studies in the target population to understand the relation between the nature of facets (attributes) of the data and the cognitive load. Secondly, we propose an algorithm to design optimum navigation trees based on the inferences made in the first phase. We compare the proposed algorithm with the traditional facet generation algorithms with respect to various factors and discuss the optimality of the proposed algorithm.