论文标题
如何种植（产品）树：电子商务类型的个性化类别建议
How to Grow a (Product) Tree: Personalized Category Suggestions for eCommerce Type-Ahead
论文作者
论文摘要
为了在搜索页面中平衡精确度和召回，领先的数字商店早在类型预先的建议中就已经有效地将用户推入了某些类别方面。在这项工作中，我们提出了SessionPath，这是一个新颖的神经网络模型，改善了两种计数：首先，该模型能够利用会话嵌入以提供可扩展的个性化；其次，SessionPath通过在分类路径中的每个节点上明确产生概率分布来预测方面。我们根据基于计数和神经模型的两家合作商店对Session Path进行基准，并展示如何以原则性的方式组合业务需求和模型行为。
In an attempt to balance precision and recall in the search page, leading digital shops have been effectively nudging users into select category facets as early as in the type-ahead suggestions. In this work, we present SessionPath, a novel neural network model that improves facet suggestions on two counts: first, the model is able to leverage session embeddings to provide scalable personalization; second, SessionPath predicts facets by explicitly producing a probability distribution at each node in the taxonomy path. We benchmark SessionPath on two partnering shops against count-based and neural models, and show how business requirements and model behavior can be combined in a principled way.
