Joint Attention Neural Model for Demand Prediction in Online Marketplaces
Published in NLDL, 2020
Recommended citation: @inproceedings{gupta2020joint, title={Joint Attention Neural Model for Demand Prediction in Online Marketplaces}, author={Gupta, Ashish and Mehrotra, Rishabh}, booktitle={Proceedings of the Northern Lights Deep Learning Workshop}, volume={1}, pages={6--6}, year={2020} }
Abstract: As an increasing number of consumers rely on online marketplaces to purchase goods from, demand prediction becomes an important problem for suppliers to inform their pricing and inventory management decisions. Business volatility and the complexity of factors influence demand, which makes it a harder quantity to predict. In this paper, we consider the case of an online classified marketplace and propose a joint multi-modal neural model for demand prediction. The proposed neural model incorporates a number of factors including product description information (title, description, images), contextual information (geography, similar products) and historic interest to predict demand. Large scale experiments on real-world data demonstrate superior performance over established baselines. Our experiments highlight the importance of considering, quantifying and leveraging the textual content of products and image quality for enhanced demand prediction. Finally, we quantify the impact of the different factors in predicting demand.