Inferring Targets from Calibrated Hesitations via Mutual Information Maximization in Multi-Behavior Recommendation

Multi-behavior recommendation enriches user preference modeling by incorporating diverse auxiliary interactions. However, most existing methods simply treat interactions without target behaviors as absolute negative feedback. This strategy ignores an important intermediate state known as user hesitation, where users exhibit strong intent but fail to complete the final conversion due to various reasons. Consequently, models cannot distinguish true disinterest from intended but hesitant behavior, which introduces substantial noise into preference modeling. To address this issue, we propose a novel framework named Calibrated Hesitation Analysis for Multi-Behavior Recommendation via Mutual Information Maximization (CHARM). Specifically, we aggregate auxiliary behaviors that lead to successful conversions into latent intent representations and train an inference network by maximizing the mutual information between these intents and observed target behaviors. We then apply this network to auxiliary behaviors without conversion, under the assumption that the conversion had occurred, in order to infer latent conversion probabilities and identify high-intent hesitation candidates. Furthermore, to distinguish genuine hesitation from interaction termination caused by competing item choices, we design a competitor substitution penalty strategy to refine hesitation confidence scores. Finally, the calibrated hesitation set is incorporated into the recommendation process to improve ranking quality. Extensive experiments on three real-world datasets demonstrate that CHARM consistently outperforms existing state-of-the-art methods. The source code is available at https://github.com/city59/CHARM.