A Big Data Recommendation Engine Framework Based on Local Pattern Analytics Strategy for Mining Multi-Sourced Big Data

Abstract

Organisations that perform business operations in a multi-sourced big data environment are in imperative need to discover meaningful patterns of interest from their diversified data sources. With the advent of big data technologies such as Hadoop and Spark, commodity hardwares play vital role in the task of data analytics and process the multi-sourced and multi-formatted big data in a reasonable cost and time. Though various data analytic techniques exist in the context of big data, recommendation system is more popular in web-based business applications to suggest suitable products, services, and items to potential customers. In this paper, we put forth a big data recommendation engine framework based on local pattern analytics strategy to explore user preferences and taste for both branch level and central level decisions. The framework encourages the practice of moving computing environment towards the data source location and avoids forceful integration of data. Further it assists decision makers to reap hidden preferences and taste of users from branch data sources for an effective customer campaign. The novelty of the framework has been evaluated in the benchmark dataset, MovieLens100k and results clearly confirm the advantages of the proposal.