Estimation models of user skills based on web search logs

Abstract

The number of Internet users has been increasing in Japan, especially the elderly. Even though there are differences in the skills of the elderly, no effective method of personalizing the user interface to suit skill level has been proposed. To solve this problem, conventionally, questionnaires or tests are used to evaluate user skills, but users find them burdensome. In order to evaluate user skills automatically, we focus on the logs of user operations. This study uses machine learning to build models that can estimate skill level from operation logs on tablet PC. First, we investigate and identify the 6 key skills necessary for effective Web search. Second, the skill evaluation tasks and the Web search tasks are created and then performed by elderly users. During the Web search tasks, the operation logs such as screen touch behavior are gathered by the Web browser. Finally, decision tree-based estimation models of the 6 skills are built. The results confirm that models can very accurately estimate skill level.