  Building recommendation algorithms is one of the most challenging tasks in
Machine Learning. Although most of the recommendation systems are built on
explicit feedback available from the users in terms of rating or text, a
majority of the applications do not receive such feedback. Here we consider the
recommendation task where the only available data is the records of user-item
interaction over web applications over time, in terms of subscription or
purchase of items; this is known as implicit feedback recommendation. There is
usually a massive amount of such user-item interaction available for any web
applications. Algorithms like PLSI or Matrix Factorization runs several
iterations through the dataset, and may prove very expensive for large
datasets. Here we propose a recommendation algorithm based on Method of Moment,
which involves factorization of second and third order moments of the dataset.
Our algorithm can be proven to be globally convergent using PAC learning
theory. Further, we show how to extract the parameters using only three passes
through the entire dataset. This results in a highly scalable algorithm that
scales up to million of users even on a machine with a single-core processor
and 8 GB RAM and produces competitive performance in comparison with existing
algorithms.
