Click-Conversion Multi-Task Model with Position Bias Mitigation for
  Sponsored Search in eCommerce
Yibo Wang Yanbing Xue Bo Liu Musen Wen Wenting Zhao Stephen Guo Philip S. Yu
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.