Time Effective Vehicle Booking: A Novel Data Online Cluster Search Recommender System

Abstract

In mundane life of everyone, people are tending towards extracting some external knowledge provided by recommendations so as to get decisive about an artifact of interest. Recommender systems or sometimes also called as recommender engines automates the process of recommendations. They are of three types: content based approach, collaborative approach and hybrid approach. In this thesis improvisation of hybrid approach is executed by proposing and developing a Data Rendering Cluster Search approach. Mechanisms of both the approaches are almost similar, basic difference though lies in the technique of portioning of the dataset fed to both the algorithms. In Data Rendering Cluster Search approach location is partitioned forming clusters and searching is done into the cluster nearest to the user to provide cab recommendations, thereby giving better time minimization than hybrid approach. Data Rendering Cluster Search is user friendly and easy to anticipate. It provides better time efficiency and high accuracy than former approach and thus can be counted as one of the best location based recommender system.