Advancing Recommendation Systems at Xueqiu: Transitioning from Point-Wise CTR Prediction to Pair-Wise TF-Ranking
This article explores the evolution of recommendation algorithms at Xueqiu, highlighting the limitations of traditional point-wise click-through rate prediction models and detailing the ongoing transition to a pair-wise TF-Ranking framework designed to mitigate user and content biases while significantly enhancing overall recommendation accuracy and user experience.
Xueqiu, recognized as China's largest stock investment community, leverages advanced technology as a core enabler to fulfill its mission of connecting investors and delivering high-quality financial content. The platform continuously optimizes its recommendation infrastructure to better serve diverse user needs.
In terms of algorithmic evolution, traditional point-wise click-through rate (CTR) prediction models exhibit inherent limitations by estimating absolute click probabilities without adequately accounting for biases across different users and content categories. To address these challenges, the algorithm engineering team is actively implementing a pair-wise solution based on TF-Ranking. This approach is expected to significantly improve ranking accuracy, reduce systemic bias, and ultimately deliver a more personalized and satisfying user experience.
References include foundational research on Wide & Deep Learning for Recommender Systems, official TensorFlow repositories for model implementation and serving, and the official Xueqiu platform.
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