Low-Complexity Decentralized Recommendation System With Similarity Constraints

In recommendation systems (RSs), nowadays, not only the traditional user-item rating matrices but also more additional information like contents, contexts, trust friends and other auxiliary information are available to enhance the performance of RS, leading to content-aware, context-aware, trust-aware RS, etc. Thus, it provides much potential to take into consideration the additional information in RS. Hence, we focus on a general low-rank matrix factorization (LRMF) model with similarity constraints and propose a decentralized algorithm based on alternating direction method of multipliers (ADMM) to relieve the computation burden in each server while preserving privacy. What's more, we utilize low-complexity skills in numerical analysis to reduce the computational complexity, based on the exploitation of the special form of the problem. Finally, simulations are performed to validate the effectiveness of our algorithms.