Mining massive online location-based services from user activity using best first gradient boosted distributed decision tree

M. Venkatesh, V. Mohan Raj and Y. Suresh

International Journal of Enterprise Network Management, 2020, vol. 11, issue 1, 3-13

Abstract: User activity is predicted through the frequency in which the online substances in location-based social networks (LBSN) are produced and used by the consumer. Users are classified by researchers into a number of groups depending upon the level of their functioning. This work involves gradient boosted distributed decision tree (GBDT) which is optimised on the basis of total iterations and shrinkage on using best algorithm. Implementation of the data is done through Hadoop network. A foursquare dataset is created using work, food, travel, park and shop. One of the most commonly used machine learning algorithm is stochastic gradient boosted decision trees (GBDT) at present. The node with lowest lower bound is developed through best first search (BFS). Its own filing system is provided through Hadoop which is called Hadoop distributed file system (HDFS). The algorithm used is K-nearest Neighbour (KNN) classifier algorithm.

Keywords: user activity; foursquare dataset; stochastic gradient boosted decision trees; GBDT; best-first search; BFS; K-nearest neighbour classifier; KNN; social network; location-based social networks; LBSN; big data; Hadoop network; Hadoop distributed file system; HDFS. (search for similar items in EconPapers)
Date: 2020
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