Author(s): S. Vinodhini, V. Rajalakshmi, B. Govindarajulu
Published in: International Journal of Engineering Research & Technology
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Volume/Issue: Vol. 3 - Issue 4 (April- 2014)
Recommender systems are found in many e-commerce applications today. Recommender systems usually provide the user with a list of recommendations that they might prefer, or supply predictions on how much the user might prefer each item. Two common approaches for providing recommendations are collaborative filtering and content based filtering. By combining these two approaches, hybrid recommendation systems can be developed that considers both the ratings of the user and the item’s feature to recommend the items to the user. The features of limited amount of data can be analyzed with the existing data analysis tools but when considering an e-book dataset of size in Terabytes, a big data analysis tool such as Hadoop is used. Hadoop is a software framework for distributed processing of large data sets. Hadoop uses MapReduce paradigm to perform distributed processing over clusters of computers to reduce the time involved in analyzing the item’s feature (keywords of a book). The proposed system is reliable and fault tolerant when compared to the existing recommendation systems as it collects the ratings from the user to predict the interest and analyses the item to find the features. The system is also adaptive as it updates the rating list frequently and finds the updated interest of the user. Experimental results show that the proposed system is more accurate than the existing recommender systems.
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