Multimodal CTR Estimation for Cold-Start Content Recommendation
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Solution Overview
Problem
Existing content recommendation systems prioritize popular content based on impression rates, neglecting quality content with low initial impressions, leading to low pushing quality and efficiency.
Innovation Solution
A method and apparatus that combines text and image eigenvectors to estimate a click-through rate (CTR) for content, using machine learning and computer vision to analyze description texts and images, and generate an estimated CTR for improved content recommendation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If content recommendation is based on impression rates and popularity, then popular content can be effectively promoted, but quality content with low initial impressions is neglected resulting in low pushing quality and efficiency
Solution Approach 1:
The system performs preliminary feature extraction on both text and image content before recommendation decisions are made. By extracting text eigenvectors through NLP and image eigenvectors through computer vision in advance, the system creates a comprehensive multi-modal feature representation that enables accurate quality assessment of content regardless of its current impression rate, allowing quality content to be identified and promoted even in cold start stages
Solution Approach 2:
The system combines multiple types of features (text eigenvectors and image eigenvectors) into a unified multi-modal feature vector, analogous to composite materials. This fusion of different feature types creates a more robust and accurate representation of content quality than either modality alone, enabling the system to accurately assess and recommend quality content while maintaining high pushing efficiency
2Measurement precision
If multi-modal feature extraction is performed on both text and image, then CTR estimation accuracy is improved, but system complexity increases
Solution Approach 1:
The system divides the complex task of CTR estimation into separate processing streams for text and image modalities. Text features are extracted through NLP models while image features are extracted through computer vision models, allowing each modality to be processed independently with optimized algorithms. This segmentation reduces overall system complexity while maintaining high CTR estimation accuracy through the subsequent fusion of the separately extracted features
Data Source
AI summary
A content processing method is disclosed, including: obtaining a description text of to-be-processed content and an image included in the to-be-processed content; performing feature extraction on the description text based on text semantics to obtain a text eigenvector; performing feature extraction on the image based on image semantics to obtain an image eigenvector; combining the text eigenvector with the image eigenvector to obtain an image-text multi-modal vector; and generating an estimated click-through rate of the to-be-processed content according to the image-text multi-modal vector.