Context-Aware Service Recommendation on High Performance Cluster

Finding and recommending suitable services on mobile devices is an important topic in today´s society where most people rely on their smartphones to provide solutions to their day-to-day problems or to receive prescribed services. Recent research has attempted to use role-based approaches to recommend mobile services to other members among the same context group. However, these proposed algorithms are inefficient and may not scale to cope with the huge amount of mobile traffics in the real-world. This paper proposes novel algorithms with better runtime complexity, and further extends them to Map Reduce style to take advantage of popular distributed computing platforms. Experiments running on a medium-sized high performance computing cluster demonstrate that our proposed algorithms outperform previous work in running time complexity and scalability.