NDLI: A Preference-Aware Service Recommendation Method on Map-Reduce

Content Provider	IEEE Xplore Digital Library
Author	Shunmei Meng Xu Tao Wanchun Dou
Copyright Year	2013
Description	Author affiliation: State Key Lab. for Novel Software Technol., Nanjing Univ., Nanjing, China (Shunmei Meng; Xu Tao; Wanchun Dou)
Abstract	Service recommender systems have shown to be valuable tools to provide appropriate recommendations to the users. However, in most of existing service recommender systems, the ratings and rankings of services presented to different users are the same, which didn't consider users' preferences and therefore could not meet users' personalized requirements. Moreover, the number of customers, alternative services and other online information grows rapidly. Thus, the improvement of scalability and efficiency of recommender systems is also necessary and urgent. In view of these challenges, a preference-aware service recommendation method on Map-Reduce, named PASR, is proposed in this paper. It aims at presenting a personalized ranking list and recommending the most appropriate services to the users from big data environment. In this method, keywords are used to indicate users' preferences, and a user based Collaborative Filtering algorithm is adopted to generate appropriate recommendations. To improve the scalability and efficiency of PASR, we implement it on a distributed computing platform, Hadoop, which uses Map-Reduce as its computing framework. Finally, experimental results show that our approach performs well both in accuracy and scalability.
Starting Page	846
Ending Page	853
File Size	584032
Page Count	8
File Format	PDF
ISBN	9780769550961
DOI	10.1109/CSE.2013.128
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-03
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Scalability Data handling Hadoop preference Vectors Thesauri Information management Data storage systems recommender system Map-Reduce keyword big data Recommender systems
Content Type	Text
Resource Type	Article

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