APRF-Net: Attentive Pseudo-Relevance Feedback Network for Query Categorization
release_hdsijcybg5aklaunkwvr2suiou
by
Ali Ahmadvand, Sayyed M. Zahiri, Simon Hughes, Khalifa Al Jadda, Surya Kallumadi, Eugene Agichtein
2021
Abstract
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.
In text/plain
format
Archived Files and Locations
application/pdf 828.1 kB
file_6b5w6lclejdwbl7acbzyxsaiva
|
arxiv.org (repository) web.archive.org (webarchive) |
2104.11384v2
access all versions, variants, and formats of this works (eg, pre-prints)