Collaborative Filtering on a Budget
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:389-396, 2010.
Matrix factorization is a successful technique for building collaborative filtering systems. While it works well on a large range of problems, it is also known for requiring significant amounts of storage for each user or item to be added to the database. This is a problem whenever the collaborative filtering task is larger than the medium-sized Netflix Prize data. In this paper, we propose a new model for representing and compressing matrix factors via hashing. This allows for essentially unbounded storage (at a graceful storage / performance trade-off) for users and items to be represented in a pre-defined memory footprint. It allows us to scale recommender systems to very large numbers of users or conversely, obtain very good performance even for tiny models (e.g. 400kB of data suffice for a representation of the EachMovie problem). We provide both experimental results and approximation bounds for our compressed representation and we show how this approach can be extended to multipartite problems.