Efficient Role Mining for Context-Aware Service Recommendation Using a High-Performance Cluster
Efficient Role Mining for Context-Aware Service Recommendation Using a High-Performance Cluster
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DOI：
10.1109/tsc.2015.2485988
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发表时间：
2017-11
影响因子：
8.1
通讯作者：
Zhiwei Yu;R. Wong;Chi-Hung Chi-Chi-Hung-Chi-36452710
中科院分区：
文献类型：
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作者：
Zhiwei Yu;R. Wong;Chi-Hung Chi-Chi-Hung-Chi-36452710
服务推荐系统一直在尝试利用上下文感知信息来推荐更好地满足服务消费者需求的服务。然而，当前的上下文感知服务推荐技术主要基于个体智能或用户的局部知识，而没有考虑不同用户之间的共同知识。为了解决这个问题，最近的研究尝试使用基于角色的方法向同一上下文组中的其他成员推荐服务。然而，这些提出的算法效率低下，并且可能无法扩展以应对现实世界中的大量移动流量。本文提出了具有更好运行时复杂性的新颖算法，并将其进一步扩展为 MapReduce 风格，以利用流行的分布式计算平台。在中型高性能计算集群上运行的实验表明，我们提出的算法在运行时复杂性和可扩展性方面优于以前的工作。
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.