Apptus Technologies and Malmö university gave a joint talk at the recommender systems conference in Hong Kong 2013. The topic regards how we can use combinatorics to maximize the number of customers that buy something from your category lister pages. This is actually quite different from trying to sell as many products as possible, since customers usually buy multiple items.
We study the problem of optimizing recommendation systems for e-commerce sites. We consider in particular a combinatorial solution to this optimization based on the well known Maximum Coverage problem that asks for the k sets (products) that cover the most elements from a ground set (consumers). This formulation provides an abstract model for what k products should be recommended to maximize the probability of consumer purchase. Unfortunately, Maximum Coverage is NP-complete but an efficient approximation algorithm exists based on the Greedy methodology.
We exhibit test results from the Greedy method on real data sets showing 3-8% increase in sales using the Maximum Coverage optimization method in comparison to the standard best-seller list. A secondary effect that our Greedy algorithm exhibits on the tested data is increased diversification in presented products over the best-seller list.
The paper (Using maximum coverage to optimize recommendation systems in e-commerce) is published in the conference proceedings of ACM RecSys 2013. You can buy the paper by following the link above. An extended version of the paper will be available on our web site in short.