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.
Highlights
In recent years, two sided marketplaces have steadily emerged as leading business models for many real world scenarios, including accommodation (Airbnb, Booking.com), app stores (Apple and Google App Store) and online shopping (Etsy, Ebay)
We propose a joint multi-view neural model for demand prediction, which jointly encodes textual information with image specific features, along with contextual and historic signals to accurately predict demand
Demand Prediction is heavily based on pricing of a product as well as traffic coming from a particular location [11]
Summary
Two sided marketplaces have steadily emerged as leading business models for many real world scenarios, including accommodation (Airbnb, Booking.com), app stores (Apple and Google App Store) and online shopping (Etsy, Ebay). These marketplaces act as intermediaries between suppliers and consumers, and facilitate transactions between the buyer and the seller. In order to efficiently manage inventory and price commodities, suppliers rely on accurately predicting demand for their products When selling such goods online, a combination of tiny, nuanced details in a product description can sway consumer interest and impact sale and revenue. Example b912c3c6a6ad Rostov Oblast Rostov-on-Don Consumer electronics Audio and video Philips bluray home theater with bluray Video, DVD and Blu-ray players
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