Collaborative Filtering Algorithm-Based Destination Recommendation and Marketing Model for Tourism Scenic Spots
Authors:
Open Access
- 28 April 2022
- journal article
- research article
- Published by Wiley in Computational Intelligence and Neuroscience
- Vol. 2022, 1-7
Abstract
The information age of rapid development of tourism industry provides abundant travel information, but it also comes with the problem of information overload, which makes it difficult to meet the growing personalized needs of people. The traditional collaborative filtering recommendation algorithm (CFA) also suffers from the problem of data sparsity when the user population increases. Therefore, this study optimizes the CFA through the similarity factor and correlation factor and enhances the tourism sense of travel experience through the satisfaction balance strategy. The experimental results show that the improved CFA method has the highest average accuracy on the overall dataset and the best recommendation performance of the satisfaction balance strategy. Overall, the recommendation model in this study is useful for attraction selection of users and marketing optimization of travel companies.Keywords
Funding Information
- Wonkwang University
This publication has 12 references indexed in Scilit:
- Enhancing Chinese Character Representation With Lattice-Aligned AttentionIEEE Transactions on Neural Networks and Learning Systems, 2021
- Dynamic Modeling Cross-Modal Interactions in Two-Phase Prediction for Entity-Relation ExtractionIEEE Transactions on Neural Networks and Learning Systems, 2021
- Personality-Aware Product Recommendation System Based on User Interests Mining and Metapath DiscoveryIEEE Transactions on Computational Social Systems, 2020
- Combined Recommendation Algorithm Based on Improved Similarity and Forgetting CurveInformation, 2019
- A social recommendation method based on an adaptive neighbor selection mechanismInformation Processing & Management, 2018
- A recommender system based on collaborative filtering using ontology and dimensionality reduction techniquesExpert Systems with Applications, 2018
- Hybrid group recommendations for a travel serviceMultimedia Tools and Applications, 2016
- A multi-criteria collaborative filtering recommender system for the tourism domain using Expectation Maximization (EM) and PCA–ANFISElectronic Commerce Research and Applications, 2015
- Recommender systems surveyKnowledge-Based Systems, 2013
- Group Recommender Systems: Combining Individual Models2010