  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.
