Recommending a diversity of product types (PTs) is important for a good shopping experience when customers are looking for products around their high-level shopping interests (SIs) such as hiking. However, the SI-PT connection is typically absent in e-commerce product catalogs and expensive to construct manually due to the volume of potential SIs, which prevents us from establishing a recommender with easily accessible knowledge systems. To establish such connections, we propose to extract PTs from the Web pages containing hand-crafted PT recommendations for SIs. The extraction task is formulated as binary HTML node classification given the general observation that an HTML node in our target Web pages can present one and only one PT phrase. Accordingly, we introduce TrENC, which stands for Tree-Transformer Encoders for Node Classification. It improves the inter-node dependency modeling with modified attention mechanisms that preserve the long-term sibling and ancestor-descendant relations. TrENC also injects SI into node features for better semantic representation. Trained on pages regarding limited SIs, TrEnc is ready to be applied to other unobserved interests. Experiments on our manually constructed dataset, WebPT, show that TrENC outperforms the best baseline model by 2.37 F1 points in the zero-shot setup. The performance indicates the feasibility of constructing SI-PT relations and using them to power downstream applications such as search and recommendation.

翻译：推荐多样化的产品类型（PT）对于顾客围绕高级购物兴趣（如徒步）寻找商品时获得良好购物体验至关重要。然而，电子商务商品目录中通常缺乏购物兴趣与产品类型之间的关联，且由于潜在购物兴趣数量庞大，人工构建这种关联成本高昂，这阻碍了利用易获取知识系统建立推荐系统。为建立此类关联，我们提出从包含为购物兴趣手工推荐产品类型的网页中提取产品类型。基于目标网页中HTML节点仅能呈现单一产品类型短语的普遍观察，我们将提取任务形式化为二值HTML节点分类问题。据此，我们提出TrENC（树形Transformer编码器节点分类模型），通过改进注意力机制保留长期兄弟关系与祖先-后代关系，增强节点间依赖建模。TrENC还将购物兴趣注入节点特征以提升语义表征。基于有限购物兴趣相关页面训练后，TrENC可直接应用于其他未见兴趣。在人工构建的WebPT数据集上的实验表明，TrENC在零样本场景下较最优基线模型F1值提升2.37个百分点。该性能验证了构建购物兴趣-产品类型关系并用于支撑搜索、推荐等下游应用的可行性。