MISS: Multi-Interest Self-Supervised Learning Framework for Click-Through Rate Prediction
This work addresses CTR prediction for recommender systems, offering a plug-in framework to improve existing models, but it is incremental as it builds on prior deep learning and self-supervised methods.
The paper tackles the problems of label sparsity, label noise, and underuse of domain knowledge in click-through rate (CTR) prediction by proposing a Multi-Interest Self-Supervised Learning (MISS) framework, which enhances feature embeddings with interest-level self-supervision signals and boosts state-of-the-art models by up to 13.55% in AUC on large-scale datasets.
CTR prediction is essential for modern recommender systems. Ranging from early factorization machines to deep learning based models in recent years, existing CTR methods focus on capturing useful feature interactions or mining important behavior patterns. Despite the effectiveness, we argue that these methods suffer from the risk of label sparsity (i.e., the user-item interactions are highly sparse with respect to the feature space), label noise (i.e., the collected user-item interactions are usually noisy), and the underuse of domain knowledge (i.e., the pairwise correlations between samples). To address these challenging problems, we propose a novel Multi-Interest Self-Supervised learning (MISS) framework which enhances the feature embeddings with interest-level self-supervision signals. With the help of two novel CNN-based multi-interest extractors,self-supervision signals are discovered with full considerations of different interest representations (point-wise and union-wise), interest dependencies (short-range and long-range), and interest correlations (inter-item and intra-item). Based on that, contrastive learning losses are further applied to the augmented views of interest representations, which effectively improves the feature representation learning. Furthermore, our proposed MISS framework can be used as an plug-in component with existing CTR prediction models and further boost their performances. Extensive experiments on three large-scale datasets show that MISS significantly outperforms the state-of-the-art models, by up to 13.55% in AUC, and also enjoys good compatibility with representative deep CTR models.