Efficient Role Mining for Context-Aware Service Recommendation Using a High-Performance Cluster

Service recommendation systems have been trying to utilize context-aware information to recommend services that better meet the needs of the service consumers. However, current context-aware service recommendation techniques are mainly based on individual intelligence or the local knowledge of users, and do not take into consideration the common knowledge among different users. To address this, recent research has attempted to use role-based approaches to recommend services to other members within the same context group. However, these proposed algorithms are inefficient and may not scale to cope with the large amount of mobile traffic in the real-world. This paper proposes novel algorithms with better runtime complexity, and further extends them to a MapReduce 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 runtime complexity and scalability.