A Trust Aware Product Recommending Scheme for Multiple Cloud using HADOOP Services

Service recommender systems have been shown as irreplaceable tools for yielding worthy recommendations to client. In the recent years, the range of client, services and online information exchange has grown rapidly, producing the big data analysis issue for service recommender systems. Accordingly, the conventional recommender systems frequently suffer from scalability and problems related to efficieny most of existing recommender systems presents the same grades and rankings to various users without considering multiple users' preferences, which fails to meet users' individualize requirements. In this work, to mention the above challenges and presenting a personalized recommendation list for products and recommending the most relevant products to the users effectively. Particularly, keywords are used to point out users' preferences, and hadoop framework is used for storing and processing the data of the client and will generate appropriate recommendations.

[1]  Huilin Liu,et al.  A Two-stage Recommendation Algorithm Based on K-means Clustering In Mobile E-commerce , 2010 .

[2]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[3]  Zibin Zheng,et al.  QoS Ranking Prediction for Cloud Services , 2013, IEEE Transactions on Parallel and Distributed Systems.

[4]  Divyakant Agrawal,et al.  Big data and cloud computing , 2010, Proc. VLDB Endow..

[5]  Greg Linden,et al.  Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .

[6]  GhemawatSanjay,et al.  The Google file system , 2003 .

[7]  Yan-Ying Chen,et al.  Travel Recommendation by Mining People Attributes and Travel Group Types From Community-Contributed Photos , 2013, IEEE Transactions on Multimedia.

[8]  Federico Alvarez,et al.  Recommender System for Sport Videos Based on User Audiovisual Consumption , 2012, IEEE Transactions on Multimedia.

[9]  Mingdong Tang,et al.  AWSR: Active Web Service Recommendation Based on Usage History , 2012, 2012 IEEE 19th International Conference on Web Services.

[10]  Zhi-Dan Zhao,et al.  User-Based Collaborative-Filtering Recommendation Algorithms on Hadoop , 2010, 2010 Third International Conference on Knowledge Discovery and Data Mining.

[11]  Ying Li,et al.  Location: A Feature for Service Selection in the Era of Big Data , 2013, 2013 IEEE 20th International Conference on Web Services.

[12]  Gert R. G. Lanckriet,et al.  Learning Content Similarity for Music Recommendation , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[13]  Sanjay Ghemawat,et al.  MapReduce: simplified data processing on large clusters , 2008, CACM.

[14]  Jinjun Chen,et al.  KASR: A Keyword-Aware Service Recommendation Method on MapReduce for Big Data Applications , 2014, IEEE Transactions on Parallel and Distributed Systems.