As online shopping becomes increasingly popular, users perform more product search to purchase items. Previous studies have investigated people's online shopping behaviours and ways to predict online purchases. However, from a user perspective, there still lacks an in-depth understanding of why users search, how they interact with, and perceive the product search results. In this paper, we address the following three questions: (1) what are the intents of users underlying their search activities? (2) do users behave differently under different search intents? and (3) how does user perceived satisfaction relate to their search behaviour as well as search intents, and can we predict product search satisfaction with interaction signals? Based on an online survey and search logs collected from a major commercial product search engine, we show that user intents in product search fall into three categories: Target Finding (TF), Decision Making (DM) and Exploration (EP). Through a log analysis and a user study, we observe different user interaction patterns as well as perceived satisfaction under these three intents. Using a series of user interaction features, we demonstrate that we can effectively predict user satisfaction, especially for TF and DM intents.
International Conference on Web Search and Web Data Mining
Centrum Wiskunde & Informatica, Amsterdam (CWI), The Netherlands

Su, N., He, J., Liu, Y., Zhang, M., & Ma, S. (2018). User intent, behaviour, and perceived satisfaction in product search. In Proceedings of the International Conference on Web Search and Data Mining (pp. 547–555). doi:10.1145/3159652.3159714