HIPA-Net: Heterogeneous Intent Projection Alignment for Feedback Calibration in CVR Prediction

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Lead advertising is a contact-oriented conversion scenario in industrial recommender systems, where the target action is to initiate follow-up communication rather than complete an immediate transaction. A key challenge in lead CVR prediction is that implicit drop-off behaviors are frequent and informative, but their feedback polarity is not directly observed. Treating such drop-off evidence as fixed negative feedback or ordinary noisy implicit feedback can distort user contact intent. We formulate lead CVR prediction as a semantic feedback calibration problem for unfinished contact behaviors. To address it, we propose HIPA-Net, a Heterogeneous Intent Projection Alignment Network. HIPA-Net learns candidate-aware representations for fine-grained positive, negative, and drop-off feedback behaviors, constructs explicit positive and negative intent anchors from reliable feedback, and calibrates implicit drop-off evidence through anchor-specific projection alignment. HIPA-Net has been deployed in a large-scale industrial lead advertising system. Offline experiments show that HIPA-Net outperforms representative behavior modeling, multi-behavior, feedback-aware, and denoising baselines. Online A/B tests further show +2.184% CTR, +5.426% RPM, and -11.521% user-side negative feedback rate, demonstrating the effectiveness of anchor-guided semantic calibration for contact-oriented recommendation.