Trigger-Induced Recommendation (TIR), which aims to predict user interest based on a trigger item, has gained considerable traction on e-commerce platforms. Current TIR methods typically analyze user intent by integrating explicit interest in the trigger item and implicit interest derived from user historical behaviors. However, these methods often overlook the contextual information and occurring scenarios related to the trigger, resulting in an undue emphasis on isolated trigger items and a consequently restrictive understanding of users' short-term intentions. To address these challenges, we propose a novel scenario-aware multi-trigger induction method featuring three key enhancements: (1) The Context Modeling Network learns contextual information associated with the trigger during the request, improving the understanding of users' real intentions regarding the trigger item; (2) The Multi-Trigger Learning Network introduces user latent triggers from various scenarios to uncover users' potential external preferences; (3) The Scenario Induction Network captures the characteristics of the scenarios in which triggers occur and performs induction to yield scenario-aware user intentions prediction. We validate our approach through experiments on multiple industrial datasets, demonstrating the model's effectiveness. Furthermore, we have integrated the model into an online advertising system, achieving a 5.46% improvement in Click-Through Rate (CTR).

Accurate Click-Through Rate (CTR) prediction is vital for search engines and recommendation systems, yet it is often hindered by the ''cold start problem'', which arises from insufficient historical data for new users. Recent approaches have sought to tackle this by training encoder-decoder networks on data from warm users to generate virtual behavior embeddings for cold users. However, these methods have shortcomings in terms of simplistic encoding techniques for warm user behaviors and direct utilization of virtual behavior embeddings, leading to limitations in user interest expression and generalization. To address these challenges, we propose a novel method that leverages Graph Isomorphism Networks (GIN) for cohort modeling within CTR prediction. GIN effectively captures high-order user-item interactions, providing a more nuanced understanding of users' diverse interests. Additionally, the cohort modeling strategy minimizes deviations in constructed embeddings, enhancing the model's generalization abilities. We validate our approach through experiments on public and industrial datasets, demonstrating significant improvements for both warm and cold users compared to existing methodologies. Furthermore, we implemented the GIN Cohort Modeling (GINCM) in a large-scale online advertising system, optimizing for both pre-computation and real-time processing to reduce latency. The implementation yields notable enhancements of 2.13% in CTR and Revenue Per Mille(RPM), showcasing the practical effectiveness and real-world applicability of our model.

Deep learning-based AI models typically require a large amount of high-quality annotated data to achieve optimal performance. However, thelabel distribution shiftcaused by noisy annotations can lead to perturbations in the classification boundary, reducing the robustness and generalization capabilities of deep learning models. To mitigate this issue, we transform the problem of learning from noisy labels into a semi-supervised learning problem, and propose a novel Semi-Supervised Distribution Alignment (SSDA) framework that strategically integrates noise-robust distribution alignment within a unified semi-supervised learning paradigm for combating noisy labels. By leveraging the similarity distribution between historical predictions, the proposed SSDA approach benefits from a flexible multi-historical regression modeling strategy, which aims to identify high-confidence samples/pairs and recalibrate the label shift through pseudo-labels. Furthermore, our approach employs a comprehensive multi-granularity distribution adaptation strategy, incorporating both instance-wise and class-aware distribution alignment to quantitatively minimize semantic discrepancies across different mixed feature domains. In this way, our SSDA approach ultimately achieves more resilient and generalizable performance against label noise, even in the presence of substantial noise. Extensive experiments conducted on multiple simulated and real-world noisy benchmark datasets consistently demonstrate the superiority and effectiveness of our SSDA method compared to existing state-of-the-art baselines.