Modified Query Expansion Through Generative Adversarial Networks for Information Extraction in E-Commerce

TL;DR

This paper introduces a novel modified query expansion method using a conditional GAN with transformer and RNN models to improve semantic relevance in e-commerce information retrieval.

Contribution

The paper presents a new mQE-CGAN framework that incorporates semantic insights as conditions, enhancing query expansion effectiveness in e-commerce search.

Findings

Semantic similarity increased by nearly 10% with the proposed method.

The framework effectively integrates semantic insights into query expansion.

Experimental results outperform baseline models in relevance metrics.

Abstract

This work addresses an alternative approach for query expansion (QE) using a generative adversarial network (GAN) to enhance the effectiveness of information search in e-commerce. We propose a modified QE conditional GAN (mQE-CGAN) framework, which resolves keywords by expanding the query with a synthetically generated query that proposes semantic information from text input. We train a sequence-to-sequence transformer model as the generator to produce keywords and use a recurrent neural network model as the discriminator to classify an adversarial output with the generator. With the modified CGAN framework, various forms of semantic insights gathered from the query document corpus are introduced to the generation process. We leverage these insights as conditions for the generator model and discuss their effectiveness for the query expansion task. Our experiments demonstrate that the utilization of condition structures within the mQE-CGAN framework can increase the semantic similarity between generated sequences and reference documents up to nearly 10% compared to baseline models