Search engine switching detection based on user personal preferences and behavior patterns

Abstract

Sometimes, during a search task users may switch from one search engine to another for several reasons, e.g., dissatisfaction with the current search results or desire for broader topic coverage. Detecting the fact of switching is difficult but important for understanding users' satisfaction with the search engine and the complexity of their search tasks, leading to economic significance for search providers. Previous research on switching detection mainly focused on studying different signals useful for the task and particular reasons for switching. Although it is known that switching is a personal choice of a user and different users have different search behavior, little has been done to understand how these differences could be used for switching detection. In this paper we study the effectiveness of learning personal behavior patterns for switching detection and present a personalized approach which uses user's session history containing sessions with and without switches. Experiments show that users' personal habits and behavior patterns are indeed among the most informative signals. Our findings can be used by a search log analyzer for engine switching detection and potentially other log mining problems, thus providing valuable signals for search providers to improve user experience.