Technical Perspective: Evaluating Sampled Metrics Is Challenging

Item recommendation algorithms rank the items in a catalogue from the most relevant to the least relevant ones for a given context (for example, query) provided in input. Such algorithms are a key component of our daily interactions with digital systems, and their diffusion in the society will only increase in the foreseeable future. Given the diffusion of recommendation systems, their comparison is a crucial endeavor. Item recommendation algorithms are usually compared using some metric (for example, average precision) that depends on the position of the truly relevant items in the ranking, produced by the algorithm, of all the items in a catalogue. The experimental evaluation and comparison of algorithms is far from easy.