Effectiveness of Improved Personalized Intelligent Intelligent Systems Recommendation Model for Travel Information by Collaborative Filtering Algorithm Incorporating User Interests

Authors

  • Yao Li Trade route institute, Chongqing College of Finance and Economics, Chongqing 402160, China
  • Xiaomeng Gou College of General Studies, Chongqing Water Resources and Electric Engineering College, Chongqing 402160, China

Abstract

A critical issue for customized intelligent travel information recommendation is the exponential growth of travel information and the wide range of travel needs. In order to address the issues of cold start and poor recommendation results yielded by the recommendation system, in this study, a collaborative filtering method is proposed that integrates user interests. This algorithm is an item-oriented recommendation algorithm called iExpand algorithm. The algorithm views user conduct as a collection of relatively implicit interests that need to be examined, with the specific preferences of each individual user being considered. As a result, the algorithm is better able to examine the user’s interests and grasp how those interests change over time. The iExpand algorithm has a 0.962 accuracy, a 0.038 loss rate, a 0.033 suggestion error, and a runtime of 1.04s. The findings for the customized intelligent recommendation model for trip information showed that the proposed algorithm has a greater recommendation accuracy and a quicker reaction time, resolving the issues of cold start.

Keywords: iExpand; user interests; collaborative filtering algorithm; personalisation; travel information

Cite As

Y. Li, X. Gou, "Effectiveness of Improved Personalized Intelligent Intelligent Systems Recommendation Model for Travel Information by Collaborative Filtering Algorithm Incorporating User Interests", Engineering Intelligent Systems, vol. 33 no. 2,
pp. 169-178, 2025.




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Published

2025-03-01

Issue

Vol. 33 No. 2 (2025): International Journal of Engineering Intelligent Systems

Section

Article

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