Octet: Online Catalog Taxonomy Enrichment with Self-Supervision
This addresses the labor-intensive and scalability issues in maintaining online catalog taxonomies for applications like item categorization and search, though it is an incremental improvement over existing methods.
The paper tackles the problem of enriching incomplete online catalog taxonomies, which are typically maintained manually and difficult to scale, by proposing Octet, a self-supervised framework that leverages heterogeneous data like user queries and items; it achieves a 2 times larger taxonomy in open-world evaluation, outperforming state-of-the-art methods.
Taxonomies have found wide applications in various domains, especially online for item categorization, browsing, and search. Despite the prevalent use of online catalog taxonomies, most of them in practice are maintained by humans, which is labor-intensive and difficult to scale. While taxonomy construction from scratch is considerably studied in the literature, how to effectively enrich existing incomplete taxonomies remains an open yet important research question. Taxonomy enrichment not only requires the robustness to deal with emerging terms but also the consistency between existing taxonomy structure and new term attachment. In this paper, we present a self-supervised end-to-end framework, Octet, for Online Catalog Taxonomy EnrichmenT. Octet leverages heterogeneous information unique to online catalog taxonomies such as user queries, items, and their relations to the taxonomy nodes while requiring no other supervision than the existing taxonomies. We propose to distantly train a sequence labeling model for term extraction and employ graph neural networks (GNNs) to capture the taxonomy structure as well as the query-item-taxonomy interactions for term attachment. Extensive experiments in different online domains demonstrate the superiority of Octet over state-of-the-art methods via both automatic and human evaluations. Notably, Octet enriches an online catalog taxonomy in production to 2 times larger in the open-world evaluation.