Title: A Model-based Projection Technique for Segmenting Customers,
Abstract: We consider the problem of segmenting a large population of customers into
non-overlapping groups with similar preferences, using diverse preference
observations such as purchases, ratings, clicks, etc. over subsets of items. We
focus on the setting where the universe of items is large (ranging from
thousands to millions) and unstructured (lacking well-defined attributes) and
each customer provides observations for only a few items. These data
characteristics limit the applicability of existing techniques in marketing and
machine learning. To overcome these limitations, we propose a model-based
projection technique, which transforms the diverse set of observations into a
more comparable scale and deals with missing data by projecting the transformed
data onto a low-dimensional space. We then cluster the projected data to obtain
the customer segments. Theoretically, we derive precise necessary and
sufficient conditions that guarantee asymptotic recovery of the true customer
segments. Empirically, we demonstrate the speed and performance of our method
in two real-world case studies: (a) 84% improvement in the accuracy of new
movie recommendations on the MovieLens data set and (b) 6% improvement in the
performance of similar item recommendations algorithm on an offline dataset at
eBay. We show that our method outperforms standard latent-class and
demographic-based techniques.