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.

翻译：位置偏差（用户倾向于关注搜索结果列表中排名靠前的项目，而忽略其与查询的相关性）在许多排序系统中普遍存在。训练数据中的位置偏差会导致排序模型产生偏差，从而造成项目排名、点击率（CTR）和转化率（CVR）预测愈发不公平。为同时缓解项目点击率和转化率预测中的位置偏差，我们提出了两种无位置偏差的CTR与CVR预测模型：位置感知点击-转化模型（PACC）及基于位置嵌入的PACC（PACC-PE）。PACC基于概率分解构建，将位置信息建模为概率形式；PACC-PE则利用神经网络将产品特定的位置信息建模为嵌入向量。在电商赞助产品搜索数据集上的实验表明，我们提出的模型具有更优的排序效果，并能显著缓解CTR和CVR预测中的位置偏差。