We are interested in discovering user groups from collaborative rating datasets of the form ⟨
i, u, s ⟩, where i ∈ I, u ∈ U, and s is the integer rating that user u has assigned to item i.
Each user has a set of attributes that help find labeled groups such as young computer
scientists in France and American female designers. We formalize the problem of finding
user groups whose quality is optimized in multiple dimensions and show that it is NP-
Complete. We develop α-MOMRI, an α-approximation algorithm, and h-MOMRI, a heuristic …

We are interested in discovering user groups from collabo-rative rating datasets of the form i,
u, s, where i∈ I, u∈ U, and s is the integer rating that user u has assigned to item i. Each
user has a set of attributes that help find labeled groups such as young computer scientists
in France and American female designers. We formalize the problem of finding user groups
whose quality is optimized in multiple dimensions and show that it is NP-Complete. We
develop α-MOMRI, an α-approximation algorithm, and h-MOMRI, a heuristic-based …