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A switching hybrid mobile recommender system for tourists

A switching hybrid mobile recommender system for tourists 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Information and Decision Sciences Inderscience Publishers

A switching hybrid mobile recommender system for tourists

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1756-7017
eISSN
1756-7025
DOI
10.1504/IJIDS.2020.106735
Publisher site
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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.

Journal

International Journal of Information and Decision Sciences – Inderscience Publishers

Published: Jan 1, 2020

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