Enhancing Ecommerce Recommenders with Dual Transformer Models

Abstract

The growing complexity and scale of ecommerce platforms have created a need for more advanced and precise recommendation systems. Traditional recommendation approaches, such as collaborative filtering and content-based models, while effective, often struggle to fully capture the evolving preferences and behaviours of users in dynamic online marketplaces. To address these limitations, this paper explores the application of dual transformer models in enhancing ecommerce recommender systems. Transformer models, originally developed for natural language processing, offer powerful attention mechanisms that allow for the processing of sequential and contextual data, providing a more nuanced understanding of user behaviour.

This study introduces dual transformer models, which simultaneously process both user interaction history and item features to improve recommendation accuracy. By capturing the intricate relationships between users and items from both dimensions, dual transformer models offer a bidirectional approach that can adapt to changing user preferences in real time. This enables ecommerce platforms to deliver more relevant, personalized, and context-aware product recommendations.

The use of dual transformers in recommendation systems presents several benefits, including improved scalability, enhanced personalization, and increased user engagement. By addressing the shortcomings of traditional recommender systems, dual transformer models have the potential to revolutionize the ecommerce industry, leading to better user satisfaction and higher conversion rates. This paper highlights the potential of these models in improving the overall effectiveness of ecommerce recommendation engines, making them a valuable tool for modern online shopping platforms.