Long Term Recommender Benchmarking for Mobile Shopping List Applications using Markov Chains

This paper presents a method to estimate the performance and success rate of a recommender system for digital shopping lists. The list contains a number of items that are allowed to occupy three different states (to be purchased, purchased and deleted) as a function of time. Using Markov chains, the probability distribution function over time can be estimated for each state, and thus, the probability that a recommendation is deleted from the list can be used to benchmark a recommender on its endurance and performance. An experimental set up is described that allows to test the presented method in an actual mobile application. The application of the method will allow to benchmark a variety of recommenders. An outlook is given on how the presented method can be used iteratively to support a recommender in finding the user's favorite items/products.