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Recommendation of Services By Mapping Categorical Preferences With Past User Comments Using Hadoop

Publication Date : 10/07/2015

Author(s) :

Ms.Ruchita V. Tatiya , Prof. Archana S. Vaidya.

Conference Name :
4th International Conference on Recent Trends in Engineering & Technology(ICRTET-2015) July 2-4,2015 Organized by SNJB's KBJ College of Engineering,Chandwad,Nashik,Maharashtra,India

Abstract :

Recommendation of services attempts to give the guidance to the users regarding the useful services based on their personalized preferences, past behavior or their similar likings with the other users. The various approaches of recommendation systems, like content-based, collaborative filtering, hybrid, etc, can further be classified according to their algorithmic technique as memorybased (heuristic) or model-based recommendation algorithms. Ratings and rankings are only being considered by many existing recommendation systems, ignoring the categorical preferences and likings of the users. Big Data, a major issue, is contributing to a large amount of data which is not only difficult to capture, store, manage and analyze but is also affecting many recommender systems with inefficiency and scalability problems. Proposed system enhances existing recommendation systems to generate recommendations based on the categorical preferences of the present user by matching them with the reviews/comments of the past users’. Keywords are used to indicate the preferences of present and past users and similarity between them is computed using methods like approximate and exact similarity computation. Semantic analysis is performed on the reviews to eliminate the unnecessary reviews of the users. To improve the performance of recommendation system in big data environment it can be ported on distributed computing platform, Hadoop which uses a Map-reduce computing framework.

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