KASR: A Keyword-Aware Service Recommendation Method on MapReduce for Big Data Applications
KASR: A Keyword-Aware Service Recommendation Method on MapReduce for Big Data Applications
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DOI：
10.1109/tpds.2013.2297117
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发表时间：
2014-01
影响因子：
5.3
通讯作者：
Shunmei Meng;Wanchun Dou;Xuyun Zhang;Jinjun Chen
中科院分区：
文献类型：
--
作者：
Shunmei Meng;Wanchun Dou;Xuyun Zhang;Jinjun Chen
服务推荐系统已被证明是向用户提供适当推荐的有价值的工具。在过去的十年中，客户，服务和在线信息的数量迅速增长，产生了服务推荐系统的大数据分析问题。因此，传统的服务推荐系统在处理或分析此类大规模数据时经常遇到可扩展性和效率低下的问题。此外，现有的服务推荐系统大多对不同用户呈现相同的服务评级和排名，而没有考虑不同用户的偏好，因此无法满足用户的个性化需求。在本文中，我们提出了一个关键字感知的服务推荐方法，命名为KASR，以解决上述挑战。它的目的是提供一个个性化的服务推荐列表，并有效地向用户推荐最合适的服务。具体来说，使用关键字来指示用户的偏好，并采用基于用户的协同过滤算法来生成合适的推荐。为了提高其在大数据环境中的可扩展性和效率，KASR在Hadoop上实现，Hadoop是一种使用MapReduce并行处理范式的广泛采用的分布式计算平台。最后，在真实数据集上进行了大量的实验，结果表明，KASR显着提高了服务推荐系统的准确性和可扩展性比现有的方法。
Service recommender systems have been shown as valuable tools for providing appropriate recommendations to users. In the last decade, the amount of customers, services and online information has grown rapidly, yielding the big data analysis problem for service recommender systems. Consequently, traditional service recommender systems often suffer from scalability and inefficiency problems when processing or analysing such large-scale data. Moreover, most of existing service recommender systems present the same ratings and rankings of services to different users without considering diverse users' preferences, and therefore fails to meet users' personalized requirements. In this paper, we propose a Keyword-Aware Service Recommendation method, named KASR, to address the above challenges. It aims at presenting a personalized service recommendation list and recommending the most appropriate services to the users effectively. Specifically, keywords are used to indicate users' preferences, and a user-based Collaborative Filtering algorithm is adopted to generate appropriate recommendations. To improve its scalability and efficiency in big data environment, KASR is implemented on Hadoop, a widely-adopted distributed computing platform using the MapReduce parallel processing paradigm. Finally, extensive experiments are conducted on real-world data sets, and results demonstrate that KASR significantly improves the accuracy and scalability of service recommender systems over existing approaches.