A Reference Dependence Approach to Enhancing Early Prediction of Session Behavior and Satisfaction

There is substantial evidence from behavioral economics and decision sciences demonstrating that in the context of decision-making under uncertainty, the carriers of value behind actions are gains and losses defined relative to a reference point (e.g. pre-action expectations), rather than the absolute final outcomes. Also, the capability of early predicting session-level search decisions and user experience is essential for developing reactive and proactive search recommendations. To address these research gaps, our study aims to 1) develop reference dependence features based on a series of simulated user expectations or reference points in first query segments of sessions, and 2) examine the extent to which we can enhance the performance of early predicting session behavior and user satisfaction by constructing and employing reference dependence features. Based on the experimental results on three datasets of varying types, we found that incorporating reference dependent features developed in first query segments into prediction models achieves better performance than using baseline cost-benefit features only in early predicting three key session metrics (user satisfaction score, session clicks, and session dwell time). Also, when running simulations by varying the search time expectation and rate of user satisfaction decay, the results demonstrate that users tended to expect to complete their search within a minute and showed a rapid rate of satisfaction decay in a logarithmic fashion once surpassing the estimated expectation points. By factoring in a user’s search time expectation and measuring their behavioral response once the expectation is not met, we can further improve the performance of early prediction models and enhance our understanding of users’ behavioral patterns. CCS CONCEPTS • Information systems → Users and interactive retrieval.