Scalable Query Understanding for E-commerce: An Ensemble Architecture with Graph-based Optimization
Authors: Giuseppe Di Fabbrizio, Evgeny Stepanov, Ludovico Frizziero, Filippo Tessaro (VUI Inc., 2024)
Topic: Chatbots and Dialogue Systems

Abstract
Query understanding is a critical component in e-commerce platforms, facilitating accurate interpretation of user intent and efficient retrieval of relevant products. This study investigates scalable query understanding techniques applied to a real-world use case in the e-commerce grocery domain. We propose a novel architecture that integrates deep learning models with traditional machine learning approaches to capture query nuances and deliver robust performance across diverse query types and categories. Experimental evaluations conducted on real-life datasets demonstrate the efficacy of our proposed solution in terms of both accuracy and scalability. The implementation of an optimized graph-based architecture utilizing the Ray framework enables efficient processing of high-volume traffic. Our ensemble approach achieves an absolute 2% improvement in accuracy over the best individual model. The findings underscore the advantages of combining diverse models in addressing the complexities of e-commerce query understanding.

Introduction
Accurately understanding and classifying user queries is crucial for providing a seamless shopping experience by boosting the product search results relevance in e-commerce AUTHOR. Query understanding enables e-commerce platforms to interpret users' intents, retrieve relevant products, and personalize the user's journey through the shopping experience. However, the task of query understanding in e-commerce presents several challenges due to the diverse nature of queries, the large-scale product catalogs, and the need for efficient processing of high-volume traffic with noisy behavioral signals AUTHOR. Query understanding in e-commerce involves multiple sub-tasks, such as query classification, entity recognition, and intent detection. Query classification aims to categorize user queries into predefined product categories, facilitating improved product retrieval and ranking AUTHOR. Entity recognition identifies key information within the query, such as brand names, product attributes, and numerical values, which can be used to refine the search results AUTHOR. Intent detection focuses on understanding the user's underlying goal, such as product discovery, comparison, or purchase AUTHOR. One of the primary challenges in query understanding is the inherent ambiguity and diversity of user queries. E-commerce queries are often short, lacking context, and can have multiple interpretations AUTHOR. Moreover, the large-scale product catalogs in e-commerce platforms, spanning thousands of categories and millions of products, pose a significant challenge in accurately mapping queries to relevant categories and products. Various approaches have been proposed to address these challenges, leveraging traditional machine learning techniques and deep learning models. Rule-based systems and keyword matching have been widely used for query classification and entity recognition AUTHOR. However, these approaches often struggle with the variability and complexity of natural language queries. Different query intents require different algorithms to yield optimum results AUTHOR. Queries can be classified into navigational (e.g., product category, brand, title) and informational (e.g., product-related questions). While navigational queries require exact matching to catalog products, informational queries necessitate applying more complex understanding techniques. Another critical aspect of query understanding in e-commerce is efficiently processing high-volume traffic. E-commerce platforms receive millions of queries daily, requiring scalable and real-time query understanding systems. Distributed computing frameworks, such as Apache Spark and Ray, have been employed to parallelize query processing and handle the massive scale of e-commerce data AUTHOR. In this paper, we propose an ensemble approach for query understanding in e-commerce, combining deep learning models and traditional techniques. Our approach leverages the strengths of both deep learning, such as DistilBERT AUTHOR, and traditional models, including logistic regression and rule-based systems. By integrating these diverse models, we aim to capture the nuances of user queries and provide robust performance across various query types and categories. We introduce an optimized graph-based architecture based on the Ray framework AUTHOR, enabling efficient processing of high-volume traffic and ensuring scalability. [htbp] \centering \subfloat[\centering Query understanding parsing]{ \includegraphics[width=0.50\linewidth]{figures/qu-parsing-example.pdf \qquad \subfloat[\centering Search results ]{ \includegraphics[width=0.40\linewidth]{figures/search-example.pdf

Conclusion
This paper proposed a novel ensemble approach for query understanding in e-commerce, combining deep learning models like DistilBERT with traditional techniques like logistic regression and rule-based systems. The ensemble architecture aimed to capture the nuances of user queries and provide robust performance across query types and categories. Data augmentation techniques were employed to improve the DistilBERT model's handling of brands, misspellings, and short queries. An optimized graph-based architecture using the Ray framework enabled efficient, scalable processing of high-volume traffic. While the ensemble performed well, there are limitations to address in future work. The system focused only on navigational queries for product categorization and entity extraction. Extending it to handle informational and other query types could further improve relevance. Exploring more advanced data augmentation, model compression, and hardware acceleration techniques could enhance accuracy and efficiency. The query understanding ensemble demonstrated the value of combining diverse models and leveraging distributed computing frameworks for scalability in e-commerce search engines. E-commerce platforms can benefit from adopting similar, ensemble-based approaches customized to their query traffic and product data. The architecture enables efficient real-time query processing while meeting strict latency requirements, critical for delivering a seamless shopping experience.