Title: Evolving recommendations from past travel sequences using soft computing techniques

Authors: Sunita Tiwari; Saroj Kaushik

Addresses: School of IT, IIT Delhi, 110016, India; Department of Computer Science and Engineering, ABES Engineering College, Ghaziabad, India ' Department of Computer Science and Engineering, IIT Delhi, 110016, India

Abstract: The World Wide Web (WWW) and mobile devices have become an indispensable part of life in this time. The pervasiveness of location acquisition technologies like global positioning system (GPS) has enabled the convenient logging of the movement sequences of users using mobile devices. This work proposes a personalised tourist spot recommender system for mobile users using genetic algorithm (GA) for a situation when explicit user ratings for tourist spots are not available. Implicit ratings of users for tourist spots are mined using GPS trajectory logs. GA is used to evolve ratings of unvisited spots using implicit ratings. GPS trajectory dataset of 178 users collected by Microsoft Research Asia's GeoLife project is used for the purpose of evaluation and experiments. We emphasise that proposed approach is comparable with existing related approaches when compared in terms of average root mean squared error (RMSE) and provides focused, personalised and relevant recommendations.

Keywords: personalised recommender systems; GPS log mining; genetic algorithms; user preference discovery; soft computing; location recommender.

DOI: 10.1504/IJCSE.2017.084156

International Journal of Computational Science and Engineering, 2017 Vol.14 No.3, pp.242 - 254

Received: 03 Feb 2015
Accepted: 10 Jul 2015

Published online: 16 May 2017 *

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