Generating and Personalizing Bundle Recommendations on Steam

Many websites offer promotions in terms of bundled items that can be purchased together, usually at a discounted rate. 'Bundling' may be a means of increasing sales revenue, but may also be a means for content creators to expose users to new items that they may not have considered in isolation. In this paper, we seek to understand the semantics of what constitutes a 'good' bundle, in order to recommend existing bundles to users on the basis of their constituent products, as well the more difficult task of generating new bundles that are personalized to a user. To do so we collect a new dataset from the Steam video game distribution platform, which is unique in that it contains both 'traditional' recommendation data (rating and purchase histories between users and items), as well as bundle purchase information. We assess issues such as bundle size and item compatibility, and show that these features, when combined with traditional matrix factorization techniques, can lead to highly effective bundle recommendation and generation.