Transferable and Efficient: Unifying Dynamic Multi-Domain Product Categorization (2023)

Abstract: As e-commerce platforms develop different business lines, a special but challenging product categorization scenario emerges, where there are multiple domain-specific category taxonomies and each of them evolves dynamically over time. In order to unify the categorization process and ensure efficiency, we propose a two-stage taxonomy-agnostic framework that relies solely on calculating the semantic relatedness between product titles and category names in the vector space. To further enhance domain transferability and better exploit cross-domain data, we design two plug-in modules: a heuristic mapping scorer and a pretrained contrastive ranking module with the help of meta concepts, which represent keyword knowledge shared across domains. Comprehensive offline experiments show that our method outperforms strong baselineson three dynamic multi-domain product categorization (DMPC) tasks,and online experiments reconfirm its efficacy with a5% increase on seasonal purchase revenue. Related datasets will be released.

## introduction product categorization @xcite is a specialized text classification task that classifies product titles or descriptions into a pre-defined taxonomy of categories. as businesses expand, major e-commerce platforms (e.g., amazon and alibaba) are encountering increasingly complex scenarios, where there are multiple domain-specific category taxonomies and each of them evolves dynamically over time. we define it as dynamic multi-domain product categorization (dmpc), which simultaneously considers the following multi-domain taxonomies and taxonomy evolving challenges. in real-world businesses, e-commerce platforms usually maintain multiple business lines with relatively independent taxonomies. these business lines are catering for different customer demands or specific domain applications, for example, one provides express delivery while another specializes in low-price bargains. multiple business domains correspond to different category taxonomy structures, with various depths and distinct literal expressions of category names. conventional industry approaches train separate classifiers on each domain, which under-utilize the cross-domain data and their shared knowledge while raising the expenses of maintenance. meanwhile, with the expansion and reorganization of businesses, each category taxonomy keeps evolving as well, where old categories might be deleted or integrated and new categories are possibly added. conventional multi-class classifiers need to be re-trained every time taxonomy changes, which disrupts the operation and further diminishes the maintenance efficiency. to mitigate taxonomy evolving issues, intuitively, we reformulate the canonical text classification problem as a text relevance matching problem. moreover, to ensure both accuracy and online efficiency, we propose a two-stage taxonomy-agnostic label retrieval (talr) framework (see figure 1 ) capturing semantic similarity between a product title and its corresponding category names in the vector space, where candidate categories are first retrieved and then reranked for the final prediction. this reformulation is especially beneficial for evolving and newly added (zero-shot) categories as textual semantics are incorporated. to leverage cross-domain data in multi-domain taxonomies challenge, we devise two plug-in modules in both stages to enhance talr's domain transferability. these modules are centralized with "meta concepts" that appear in the product titles, which represent fine-grained keyword knowledge shared across domains (appendix b). as is shown in figure 1 , in the retrieval stage, besides the dense retrieval based on vector similarity (dense scorer), the statistical co-occurrence probability between meta concepts and category labels are exploited as well (mapping scorer) . in the reranking stage, meta concepts are incorporated with category labels as supervision signals for the contrastive pretraining of the scoring model (matching scorer). while the mapping scorer complements the superficial semantic dense retrieval with cross-domain commonsense knowledge, contrastive pretraining directly optimizes the vector space improving interdomain alignment and uniformity. details are given in section 2.2 and section 2.4. in summary, our contributions are: (1) for the first time, we address the dmpc problem and release the corresponding multi-domain datasets in chinese. (2) we propose a unified talr framework equipped with two well-designed plug-in modules empowered with meta concepts, which is robust and efficient against the two challenges in dmpc problem. (3) offline experiments on our annotated real-world dmpc datasets show talr's ability to effectively transfer knowledge across domains and generalize to new domains. the unified talr outperforms three separately-trained sota classifiers by 1.65% on overall accuracy and maintains satisfactory accuracy in taxonomy evolving conditions. online experiments reaffirm its efficacy with a 5% increase in seasonal purchase revenue. ## proposed framework our talr framework is structured into two stages: retrieval and reranking, as illustrated in figure 1 . we will zoom into each component of this framework. ## experiments in this section, we discuss experimental results under static multi-domain settings and dynamic (taxonomy evolving & new taxonomy) conditions. a brief comparison of time efficiency between talr and simple reranking is also included. ## conclusion to tackle dmpc problem, we propose a unified talr framework with two plug-in modules empowered with cross-domain meta concepts. with comprehensive experiments on real-world dmpc datasets, results under both multi-domain and taxonomy evolving conditions exhibit the transferability and maintenance efficiency of talr.