I'm trying to decide on the best similarity metric for a product recommendation system using item-based collaborative filtering. This is a shopping basket scenario where ratings are binary valued - the user has either purchased an item or not - there is no explicit rating system (eg, 5-stars). Step 1 is to compute item-to-item similarity, though I want to look at incorporating more features later on. Is the Tanimoto coefficient the best way to go for binary values? Or are there other metrics that are appropriate here? Thanks.