Collaborative filtering: Scalable approaches using restricted Boltzmann machines

Parallel to the growth of electronic commerce, recommender systems have become a very active area of research, both in the industry and in the academic world. The goal of these systems is to make automatic but personal recommendations when customers are overwhelmed with thousands of possibilities and do not know what to look for.

In that context, the object of this work is threefold. The first part consists in a survey of recommendation algorithms and emphasizes on a class of algorithms known as collaborative filtering algorithms. The second part consists in studying in more depth a specific model of neural networks known as restricted Boltzmann machines. That model is then experimentaly and extensively examined on a recommendation problem. The third part of this work focuses on how restricted Boltzmann machines can be made more scalable. Three different and original approaches are proposed and studied.

In the first approach, we revisit the learning and test algorithms of restricted Boltzmann machines in the context of shared-memory architectures. In the second approach, we propose to reformulate these algorithms as MapReduce tasks. Finally, in the third method, ensemble of RBMs are investigated. The best and the more promising results are obtained with the MapReduce approach.