Post-purchase recommendations in large-scale online marketplaces

In this paper, we propose a new method for addressing post-purchase recommendations for a dynamic marketplace. The proposed method uses the transactional data as the primary data source to mine co-purchase relationships. The item listings from the transactional data are mapped to their static `cluster' representation and a cluster-cluster directed graph is generated. Clusters have explicit definitions and thus it allows us to compute content similarity between any two nodes in the cluster-cluster graph. A large marketplace will have a long tail with respect to the demand (purchase) of the items. It is a well-known problem that pure collaborative filtering systems will be unable to provide relevant recommendations for the long tail. One of the important features of our method is in addressing the issue of sparse transactional data. In addition to computing cluster-cluster relationships, we also compute category-category relationships. When our system does not have sufficient data to compute related clusters for a given cluster using the cluster-cluster graph, we use category-category graph, to first find related categories for a given cluster. We show experimental A/B test results showing significant improvement over a previously reported system that solves the same complex problem.