# Product Demand Prediction with Spatial Graph Neural Networks
## 1 Introduction  
In the vast expanse of today's online marketplace, the ability to effectively sell pre-owned items hinges not just on the inherent qualities of the items themselves but also critically on the nuanced presentation within their descriptions. From vivid imagery to compelling narratives, each element plays a pivotal role in captivating potential buyers. Sellers invest considerable effort into optimizing their listings, yet they frequently encounter a disheartening obstacle: despite a meticulously optimized product listing, the anticipated demand may fail to materialize. This scenario leaves sellers in a quandary, particularly when substantial investments in marketing have been made, underscoring a pressing need for accurate demand prediction. Navigating the online marketplace demands more than just intuition; it requires a nuanced understanding of a multitude of factors that influence buyer behavior [1]. The challenge of demand prediction lies in its inherent complexity, influenced by a webof interrelated factors, including product descriptions, visual appeal, contextual cues such as geographic location, the presence of competing ads, and the intricate patterns of historical demand. These factors play a significant role in shaping customer behavior [2] and contribute to an environment where traditional sales strategies may fall short, necessitating innovative approaches to predict demand accurately. Besides, advertising demand often follows short-term and long-term patterns, including weekly and seasonal trends, thus requiring models to be able to capture these temporal dynamics [3]. The fourth Kaggle competition, leveraging the Avito dataset ([[https://www.kaggle.com/competitions/avito-demand-prediction](https://www.kaggle.com/competitions/avito-demand-prediction)]([https://www.kaggle.com/competitions/avito-demand-prediction](https://www.kaggle.com/competitions/avito-demand-prediction)) (accessed on 15 June 2024)), exemplifies this complex challenge and highlights the critical importance of accurate demand prediction in the online marketplace. Avito, as Russia's largest classified ads platform, offers a rich and diverse dataset that encapsulates the multifaceted nature of e-commerce, including the very factors mentioned above that influence buyer behavior. This competition not only tasks participants with predicting demand for a wide range of products but also emphasizes the potential impact of such predictions on crucial business decisions, from pricing strategies to marketing investments. By providing a real-world context, the competition creates a unique opportunity to test and validate innovative approaches to demand prediction, directly addressing the need for more sophisticated models in this domain. However, the path to effective demand prediction in the e-commerce domain is fraught with challenges. The heterogeneous nature of online advertising data, encompassing textual descriptions, images, and contextual information, presents a significant analytical challenge. Traditional statistical methods, while foundational, often lack the flexibility to accommodate the dynamic and multifaceted nature of e-commerce data. Machine learning techniques have introduced a degree of adaptability, yet the quest for models that can fully capture and interpret the complex interplay of factors influencing demand continues. In response to these challenges, our paper proposes a novel approach utilizing the Graph Neural Network to enhance the accuracy of e-commerce product demand prediction. By constructing a spatially aware graph representation of the marketplace and integrating advanced attention mechanisms, our methodology aims to capture the nuanced relationships and dependencies that shape demand patterns. This approach not only promises to refine demand predictions but also introduces a scalable framework adaptable to the diverse landscape of online sales. As we delve into this exploration, our paper seeks to make a significant contribution to the domain of e-commerce demand prediction.