IntExpo: Exploring Diverse Target Intents for Cross-Domain Recommendation

Cross-domain recommendation (CDR) leverages source-domain interactions to alleviate data sparsity in a target domain. Recent cross-domain sequence synthesis (CDSS) methods synthesize target-domain histories for source-only users to augment training data. However, a user's target-domain behavior depends on both that user's transferable preferences and target-specific intents. Source histories reveal transferable preferences that transfer across domains, but similar transferable preferences may lead to different target intents and behavior sequences. Existing CDSS methods often generate overly similar sequences for users with similar preferences, leaving other plausible intents underrepresented. We call this problem **Target-Intent Collapse**. To address it, we propose **IntExpo** to synthesize target-domain sequences from transferable preference profiles and diverse target-intent trajectories. Specifically, IntExpo comprises a _Transferable Preference Profiler_, an _Intent Generator_, and a _Behavior Generator_. Its Transferable Preference Profiler proposes discriminator-guided reinforcement learning (RL) to extract transferable profiles from source histories. The Intent Generator learns a conditional distribution over intent trajectories, using trajectories observed from users with similar profiles as additional supervision. Finally, the Behavior Generator produces multiple plausible target-domain sequences conditioned on a profile and diverse intent trajectories. Experiments across four cross-domain transfer settings show that IntExpo outperforms competitive baselines even under the same synthesis budget. Further analyses show that it generates diverse, plausible behaviors and alleviates the target-intent collapse problem. Our anonymous code is available at https://anonymous.4open.science/r/IntExpo-5672.