Matrix-completion-based method for cold-start of distributed recommender systems

Abstract

Recommender systems has been wildly used in many websites. These perform much better on users for which they have more information. Satisfying the needs of users new to a system has become an important problem. It is even more accurate considering that some of these hard to describe new users try out the system which unfamiliar to them by their ability to immediately provide them with satisfying recommendations, and may quickly abandon the system when disappointed. Quickly determining user preferences often through a boot process to achieve, it guides users to provide their opinions on certain carefully chosen items or categories. In particular, we advocate a matrix completion solution as the most appropriate tool for this task. We focus on online and offline algorithms that use data compression algorithm and the decision tree which has been built to do real-time recommendation. We merge the three algorithms : distributed matrix completion, cluster-based and decision-tree-based, We chose different algorithms based on different scenarios. The experimental study delivered encouraging results, with the matrix completion bootstrapping process significantly outperforming previous approaches. abstract environment.