Utilizing Non-click Samples via Semi-supervised Learning for Conversion Rate Prediction

Conversion rate (CVR) prediction is essential in recommender systems, facilitating precise matching between recommended items and users' preferences. However, the sample selection bias (SSB) and data sparsity (DS) issues pose challenges to accurate prediction. Existing works have proposed the click-through and conversion rate (CTCVR) prediction task which models samples from exposure to ``click and conversion" in entire space and incorporates multi-task learning. This approach has shown efficacy in mitigating these challenges. Nevertheless, it intensifies the false negative sample (FNS) problem. To be more specific, the CTCVR task implicitly treats all the CVR labels of non-click samples as negative, overlooking the possibility that some samples might convert if clicked. This oversight can negatively impact CVR model performance, as empirical analysis has confirmed. To this end, we advocate for discarding the CTCVR task and proposing a Non-click samples Improved Semi-supErvised (NISE) method for conversion rate prediction, where the non-click samples are treated as unlabeled. Our approach aims to predict their probabilities of conversion if clicked, utilizing these predictions as pseudo-labels for further model training. This strategy can help alleviate the FNS problem, and direct modeling of the CVR task across the entire space also mitigates the SSB and DS challenges. Additionally, we conduct multi-task learning by introducing an auxiliary click-through rate prediction task, thereby enhancing embedding layer representations. Our approach is applicable to various multi-task architectures. Comprehensive experiments are conducted on both public and production datasets, demonstrating the superiority of our proposed method in mitigating the FNS challenge and improving the CVR estimation. The implementation code is available at https://github.com/Hjh233/NISE.