Scene-wise Adaptive Network for Dynamic Cold-start Scenes Optimization in CTR Prediction
In the realm of modern mobile E-commerce, providing users with nearby commercial service recommendations through location-based online services has become increasingly vital. While machine learning approaches have shown promise in multi-scene recommendation, existing methodologies often struggle to address cold-start problems in unprecedented scenes: the increasing diversity of commercial choices, along with the short online lifespan of scenes, give rise to the complexity of effective recommendations in online and dynamic scenes. In this work, we propose Scene-wise Adaptive Network (SwAN), a novel approach that emphasizes high-performance cold-start online recommendations for new scenes. Our approach introduces several crucial capabilities, including scene similarity learning, user-specific scene transition cognition, scene-specific information construction for the new scene, and enhancing the diverged logical information between scenes. We demonstrate SwAN's potential to optimize dynamic multi-scene recommendation problems by effectively online handling cold-start recommendations for any newly arrived scenes. More encouragingly, SwAN has been successfully deployed in Meituan's online catering recommendation service, which serves millions of customers per day, and SwAN has achieved a 5.64% CTR index improvement relative to the baselines and a 5.19% increase in daily order volume proportion.
Code (1)
Tasks
Click-Through Rate PredictionDiversityMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Multi-modal Adaptive Mixture of Experts for Cold-start Recommendation
Recommendation systems have faced significant challenges in cold-start scenarios, where new items with a limited history of interaction need to be effectively recommended to users. Though multimodal data (e.g., images, t…
Recommendation SystemsMeta-Learning with Adaptive Weighted Loss for Imbalanced Cold-Start Recommendation
Sequential recommenders have made great strides in capturing a user's preferences. Nevertheless, the cold-start recommendation remains a fundamental challenge as they typically involve limited user-item interactions for …
Few-Shot LearningMeta-LearningSequential RecommendationContrastive Learning for Cold Start Recommendation with Adaptive Feature Fusion
This paper proposes a cold start recommendation model that integrates contrastive learning, aiming to solve the problem of performance degradation of recommendation systems in cold start scenarios due to the scarcity of …
Contrastive Learningfeature selectionRecommendation SystemsLarge Language Models as Data Augmenters for Cold-Start Item Recommendation
The reasoning and generalization capabilities of LLMs can help us better understand user preferences and item characteristics, offering exciting prospects to enhance recommendation systems. Though effective while user-it…
Recommendation SystemsCold-Start Active Correlation Clustering
We study active correlation clustering where pairwise similarities are not provided upfront and must be queried in a cost-efficient manner through active learning. Specifically, we focus on the cold-start scenario, where…
Active Learning