Individualized Recommender System Using Keywords With Efficient Similarity Computation Employing Map Reduce

K. Sindhuja, D Srinivas


The advancement in E-commerce enabled the websites to overwhelm  users with numerous services. Often times, users find it a challenging task to choose the appropriate and best service from the availabe services. To provide the user with the most appropriate options, Recommender system, which is an information filtering technique, can be employed.  Traditional recommender systems present the same ratings and rankings of services to different users without considering diverse users' preferences. A individualized recommender system addresses the versatile  requirements of different users. Specifically, keywords are used to indicate users' preferences, and a user-based Collaborative Filtering algorithm is adopted to generate appropriate recommendations. However, finding similarity, which is heart of any recommender system, plays a vital role. The best similarity computation method contributes massively for an efficient recommender system.

The proposed work implements three similarity computation methods. These methods are compared with the existing methods and the results proved that the proposed techniques outperformed the existing techniques. To improve its scalability and efficiency in big data environment, proposed approach is implemented on Hadoop, a widely-adopted distributed computing platform using the MapReduce parallel processing paradigm.


Shunmei Meng, Wanchun Dou, Xuyun Zhang and Jinjun Chen, “KASR: A Keyword-Aware Service Recommendation Method on MapReduce for BigData Applications” IEEE Transactions on Parallel and Distributed Systems, Vol. 25, No. 12, pp. 3221-3231, 2014.

Pablo Gamallo Otero and Stefan Bordag, “Is singular value decomposition useful for word similarity extraction?” Language resources and evaluation, Vol. 45, No. 2, pp. 95-119, 2011, Springer.

Akihiro Yamashita, Hidenori Kawamura, and Keiji Suzuki. “Similarity Computation Method for Collaborative Filtering Based on Optimization” Journal of Advanced Computational and Intelligent Informatics, Vol.14 No.6, 2010.

Y. Jing and W. Croft, “An association thesaurus for information retrieval,” Proceedings of RIAO, Vol. 94, No. 1994, pp.146-160, 1994.

Tom White, “Hadoop:The Definitive Guide, Second Edition”, O’REILLY, 2010

Jimmy Lin and Chris Dyer, “Data-Intensive Text Processing with MapReduce”, Morgan and Claypool, 2010

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