论文标题
在Multiverse购物：一种反事实归属的方法
Shopping in the Multiverse: A Counterfactual Approach to In-Session Attribution
论文作者
论文摘要
我们应对电子商务现场搜索引擎的会议归因挑战。我们将问题称为因果反事实推断，并将方法与来自行业环境的规则系统和来自多点触摸归因文献的预测模型进行对比。我们类比与正式语义的治疗方法进行了反事实，通过替代购物时间表明确对可能的结果进行建模；特别是，我们建议在目标商店学习一个生成浏览模型，以利用Prod2Vec嵌入引起的潜在空间；我们展示了如何在同一空间中有效地表示自然语言查询，以及如何执行“搜索干预”以评估因果关系的贡献。最后，我们验证了合成数据集上的方法，模仿了客户访谈和定性分析中出现的重要模式，并且我们在合作伙伴商店的行业数据集上介绍了初步发现。
We tackle the challenge of in-session attribution for on-site search engines in eCommerce. We phrase the problem as a causal counterfactual inference, and contrast the approach with rule-based systems from industry settings and prediction models from the multi-touch attribution literature. We approach counterfactuals in analogy with treatments in formal semantics, explicitly modeling possible outcomes through alternative shopper timelines; in particular, we propose to learn a generative browsing model over a target shop, leveraging the latent space induced by prod2vec embeddings; we show how natural language queries can be effectively represented in the same space and how "search intervention" can be performed to assess causal contribution. Finally, we validate the methodology on a synthetic dataset, mimicking important patterns emerged in customer interviews and qualitative analysis, and we present preliminary findings on an industry dataset from a partnering shop.
