Technical Presentations Group 2: AI for Good + Business Impact/Industry
Demand of goods in consumer goods is based on a variety of factors like price, seasonality, competitor price, geographic location, demographic data, etc. A common practice is to use some features, like geographic data and demographic data, to segment the market and build an individual model for each segment. However, with this approach we lose potentially valuable information which can be learned across segments. Hence, we propose a method for simultaneously learning multiple demand models to borrow knowledge and improve accuracy, especially for models with sparser data. For this, we propose using a neural network as a hyper-network to estimate the parameters of each demand model. Our approach leads to knowledge sharing across models as opposed to independent model fitting in each task while generating a model which is computationally tractable. Results of applying the proposed method on large-scale real data shows improved prediction accuracy and price elasticity estimates compared with the common two-step approach of clustering and using independent models.
Authors: Manu Chaudhary (PROS), Yanyan Hu (University of Houston), and Shahin Boluki (PROS)
