Click-Conversion Multi-Task Model with Position Bias Mitigation for Sponsored Search in eCommerce

• URL: http://arxiv.org/abs/2307.16060v1
• Date: Sat, 29 Jul 2023 19:41:16 GMT
• Title: Click-Conversion Multi-Task Model with Position Bias Mitigation for Sponsored Search in eCommerce
• Authors: Yibo Wang, Yanbing Xue, Bo Liu, Musen Wen, Wenting Zhao, Stephen Guo, Philip S. Yu
• Abstract summary: We propose two position-bias-free prediction models: Position-Aware Click-Conversion (PACC) and PACC via Position Embedding (PACC-PE) Experiments on the E-commerce sponsored product search dataset show that our proposed models have better ranking effectiveness and can greatly alleviate position bias in both CTR and CVR prediction.
• Score: 51.211924408864355
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Position bias, the phenomenon whereby users tend to focus on higher-ranked items of the search result list regardless of the actual relevance to queries, is prevailing in many ranking systems. Position bias in training data biases the ranking model, leading to increasingly unfair item rankings, click-through-rate (CTR), and conversion rate (CVR) predictions. To jointly mitigate position bias in both item CTR and CVR prediction, we propose two position-bias-free CTR and CVR prediction models: Position-Aware Click-Conversion (PACC) and PACC via Position Embedding (PACC-PE). PACC is built upon probability decomposition and models position information as a probability. PACC-PE utilizes neural networks to model product-specific position information as embedding. Experiments on the E-commerce sponsored product search dataset show that our proposed models have better ranking effectiveness and can greatly alleviate position bias in both CTR and CVR prediction.

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