APRF-Net: Attentive Pseudo-Relevance Feedback Network for Query Categorization
This addresses query intent understanding for e-commerce search, particularly for rare queries that lack customer behavior data, representing an incremental improvement over existing methods.
The paper tackles the problem of categorizing rare search queries in e-commerce by proposing APRF-Net, which uses pseudo-relevance feedback to enrich query representations, resulting in a 5.9% improvement in F1@1 score overall and 8.2% for rare queries.
Query categorization is an essential part of query intent understanding in e-commerce search. A common query categorization task is to select the relevant fine-grained product categories in a product taxonomy. For frequent queries, rich customer behavior (e.g., click-through data) can be used to infer the relevant product categories. However, for more rare queries, which cover a large volume of search traffic, relying solely on customer behavior may not suffice due to the lack of this signal. To improve categorization of rare queries, we adapt the Pseudo-Relevance Feedback (PRF) approach to utilize the latent knowledge embedded in semantically or lexically similar product documents to enrich the representation of the more rare queries. To this end, we propose a novel deep neural model named Attentive Pseudo Relevance Feedback Network (APRF-Net) to enhance the representation of rare queries for query categorization. To demonstrate the effectiveness of our approach, we collect search queries from a large commercial search engine, and compare APRF-Net to state-of-the-art deep learning models for text classification. Our results show that the APRF-Net significantly improves query categorization by 5.9% on F1@1 score over the baselines, which increases to 8.2% improvement for the rare (tail) queries. The findings of this paper can be leveraged for further improvements in search query representation and understanding.