Performing item-based recommendation for mining multi-source big data by considering various weighting parameters

  • Authors

    • Venkatesan Thillainayagam Research Scholar, Faculty of Computer Engineering, Pacific Academy of Higher Education and Research University, Udaipur
    • Saravanan Kunjithapatham Lokmanya College of Engineering, Navi Mumbai
    • Ramkumar Thirunavukarasu
    2018-09-17
    https://doi.org/10.14419/ijet.v7i4.16002
  • Big Data, Collaborative Filtering, Item-Based Filtering, Recommender System, Machine Learning, Big Data Mining.

  • Abstract

    In the context of big data, a recommendation system has been put forth as an efficient strategy for predicting the consumer’s pref-erences while rating items. Organizations that are functioning with multiple branches are in the imperative need for analyzing their multi-source big data to arrive novel decisions with respect to branch level and central level. In such circumstances, a multi-state business organi-zation would like to analyze their consumer preferences and enhance their decision-making activities based on the taste/preferences obtained from diversified data sources located in different places. One of the problems in current Item-based collaborative filtering approach is that users and their ratings have been considered uniformly while recording their preferences about target items. To improve the quality of rec-ommendations, the paper proposes various weighting strategies for arriving effective recommendation of items especially when the sources of data are multi-source in nature. For a multi-source data environment, the proposed strategies would be effective for validating the active user rating for a target item. To validate the novelty of the proposal, a Hadoop based big data eco-system with aid of Mahout has been con-structed and experimental investigations are carried out in a benchmark dataset.

     

     

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  • How to Cite

    Thillainayagam, V., Kunjithapatham, S., & Thirunavukarasu, R. (2018). Performing item-based recommendation for mining multi-source big data by considering various weighting parameters. International Journal of Engineering & Technology, 7(4), 2360-2365. https://doi.org/10.14419/ijet.v7i4.16002