Adversarial learning for product recommendation
This work addresses product recommendation for consumers and digital retailers, but it is incremental as it applies an existing GAN method to a new data representation.
The paper tackled product recommendation using a conditional, coupled generative adversarial network (RecommenderGAN) to learn from sparse implicit feedback data, achieving conversion rates of 1.323 to 1.763 percent, which are comparable to industry benchmarks.
Product recommendation can be considered as a problem in data fusion-- estimation of the joint distribution between individuals, their behaviors, and goods or services of interest. This work proposes a conditional, coupled generative adversarial network (RecommenderGAN) that learns to produce samples from a joint distribution between (view, buy) behaviors found in extremely sparse implicit feedback training data. User interaction is represented by two matrices having binary-valued elements. In each matrix, nonzero values indicate whether a user viewed or bought a specific item in a given product category, respectively. By encoding actions in this manner, the model is able to represent entire, large scale product catalogs. Conversion rate statistics computed on trained GAN output samples ranged from 1.323 to 1.763 percent. These statistics are found to be significant in comparison to null hypothesis testing results. The results are shown comparable to published conversion rates aggregated across many industries and product types. Our results are preliminary, however they suggest that the recommendations produced by the model may provide utility for consumers and digital retailers.