Title: A switching hybrid mobile recommender system for tourists

Authors: Bolanle Adefowoke Ojokoh; Idorenyin Akwaowo Amaunam

Addresses: Department of Computer Science, Federal University of Technology, P.M.B. 704, Akure, Nigeria ' Department of Computer Science, Akwa Ibom State University, P.M.B. 1167, Ikot Akpaden, Nigeria

Abstract: This paper proposes a switching feature-based model that leverages the needs of both new and existing users for recommendation of tourist locations. In an attempt to solve the cold-start problem, recommendations to new users are implemented with Bayesian algorithm on supplied demographic data. For existing users, the system switches to the collaborative filtering subsystem, where recommendation results are produced using Pearson correlation computation and offered based on the items in the database. The model was validated with discounted cumulative gain, precision, and recall. A comparative analysis with some existing systems showed lower mean absolute error. Experimental results obtained from the survey of different categories of users showed the effectiveness of the proposed techniques.

Keywords: Bayesian algorithm; conditional probability table; CPT; cold-start; mobile app; recommender system; decision; tourists.

DOI: 10.1504/IJIDS.2020.106735

International Journal of Information and Decision Sciences, 2020 Vol.12 No.2, pp.176 - 194

Received: 05 Jun 2018
Accepted: 04 Feb 2019

Published online: 06 Apr 2020 *

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