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DemiNet: Dependency-Aware Multi-Interest Network for CTR Prediction

Core Concepts
DemiNet improves CTR prediction by modeling multiple user interests with dependency-aware attention and self-supervised learning.
Introduction: Click-through rate (CTR) prediction is crucial for search engines, recommendations, and ads. Existing models face challenges in extracting multiple core interests and neglecting correlations between them. Proposed Model DemiNet: Utilizes dependency-aware attention for accurate item representations. Extracts multiple interests using multi-head attention on graph embeddings. Aggregates interests using interest experts and a confidence network. Experimental Results: DemiNet outperforms state-of-the-art baselines on real-world datasets. Improves overall recommendation performance significantly.
Raw user behavior sequence is noisy and intertwined, making it difficult to extract multiple core interests. Experimental results demonstrate that DemiNet significantly improves the overall recommendation performance over several state-of-the-art baselines.
"Click-through rate (CTR) prediction is one of the most important tasks in modern search engine, recommendation, and advertising systems." "Designing model to capture user’s multiple interests can further improve the performance of CTR prediction as well as the model’s interpretability."

Key Insights Distilled From

by Yule Wang,Qi... at 03-12-2024

Deeper Inquiries

How can the concept of multi-interest modeling be applied to other domains beyond CTR prediction


What are potential drawbacks or limitations of focusing on explicit user interest modeling in recommendation systems


How can self-supervised learning techniques enhance the robustness of feature extraction processes in machine learning models