Scaling Up Collaborative Filtering Data Sets through Randomized Fractal Expansions

Understand

Recommender system research suffers from a disconnect between the size of academic data sets and the scale of industrial production systems.

• In order to bridge that gap, we propose to generate large-scale user/item interaction data sets by expanding pre-existing public data sets.
• Our key contribution is a technique that expands user/item incidence matrices matrices to large numbers of rows (users), columns (items), and non-zero values (interactions).
• The proposed method adapts Kronecker Graph Theory to preserve key higher order statistical properties such as the fat-tailed distribution of user engagements, item popularity, and singular value spectra of user/item interaction matrices.