Education, Science, Technology, Innovation and Life
Open Access
Sign In
  1. Home
  2. Journals
  3. Advances in Computer, Signals and Systems
  4. Vol 7, Issue 7, 2023

Design and Implementation of Travel System for Disabled People Based on User Interest Preference Recommendation Algorithm

Download as PDF

DOI: 10.23977/acss.2023.070705 | Downloads: 21 | Views: 396

Author(s)

Yuanyuan Zhao 1, Zhenghuan Zhou 1

Affiliation(s)

1 School of Information Engineering, Suzhou University, Suzhou, China

Corresponding Author

Zhenghuan Zhou

ABSTRACT

There are a great deal of disabled persons in China's society nowadays, but they often are unable to leave the house because traveling is inconvenient for them. This work develops a recommendation system based on a recommendation algorithm that can assist persons who have disabilities navigate normally. The application of recommendation system technology can help customers rapidly identify the products they're interested in, save time, and assist companies to cut expenses. It additionally has the ability to predict users' ratings or preferences for items. In today's big data environment, there are countless recommendation systems or software for all kinds of commodities. However, giving recommendations for traveling to specific groups of people, including those who are disabled, is uncommon. In order to meet the needs of people with disabilities for barrier-free travel and to address the issue that people with disabilities have nowhere to go, this paper designs a travel recommendation system for people with disabilities based on the recommendation algorithm of users' preferences. Improve the harmony and equal treatment of Chinese society.

KEYWORDS

User interest preference, collaborative filtering, score difference, interest similarity

CITE THIS PAPER

Yuanyuan Zhao, Zhenghuan Zhou, Design and Implementation of Travel System for Disabled People Based on User Interest Preference Recommendation Algorithm. Advances in Computer, Signals and Systems (2023) Vol. 7: 42-50. DOI: http://dx.doi.org/10.23977/acss.2023.070705.

REFERENCES

[1] Wang Zheng, Zhang Cheng. Interest point recommendation algorithm based on user interest similarity and familiarity. Computer and Digital Engineering, 2018, 46 (12): 6.
[2] T R Mahesh, Vinoth Kumar V, Lim Se-Jung, UsCoTc: Improved Collaborative Filtering (CFL) recommendation methodology using user confidence, time context with impact factors for performance enhancement. PLoS One, 2023, 18: e0282904.
[3] Sun. P, J. S. Li, and G. Li. "Research on Collaborative Filtering Recommendation Algorithm Based on Sentiment Analysis and Topic Model." the 2019 4th International Conference 2019.
[4] Hou M., et al. "Development of a headspace-gas chromatography/mass spectrometry method based on matrix-matched calibration for evaluating VOC content, characterization, source, and risk in RO membrane." Polymer Testing 107 (2022): 107474.
[5] Ayub Mubbashir, Ghazanfar Mustansar Ali, Mehmood Zahid et al. Modeling user rating preference behavior to improve the performance of the collaborative filtering based recommender systems. PLoS One, 2019, 14: e0220129.
[6] Kant S., Mahara T. Merging user and item based collaborative filtering to alleviate data sparsity. Int J Syst Assur Eng Manag 9, 173–179 (2018).
[7] Wang Mingjia, Han Jingti. Collaborative filtering algorithm based on User Preference for item attributes. Computer Engineering and Applications, 2017, 53 (6): 106-110.
[8] Jin Jing, Huai Libo. Research on improved recommendation Algorithm based on tags and collaborative filtering. Journal of Yanbian University: Natural Science Edition, 2019, 45 (3): 7.
[9] W. Hong-xia, "An Improved Collaborative Filtering Recommendation Algorithm," 2019 IEEE 4th International Conference on Big Data Analytics (ICBDA), Suzhou, China, 2019, pp. 431-435.
[10] Hang Lu, Zhibin Shi, Zhongbao Liu."Collaborative Filtering Recommendation Algorithm Based on user interest and rating Differences." Computer Engineering and Applications 56. 7 (2020): 6.
[11] Dietz L. W., Sertkan M., Myftija S., Palage S. T., Neidhardt J., & Worndl W. (2022). A comparative study of data-driven models for travel destination characterization. Frontiers in big data, 5.
[12] He Wenkun, Wang Yuying, Shi Jiarong, et al. Collaborative filtering recommendation algorithm based on the number and difference degree of common scores. Journal of Yili Normal University (Natural Science Edition), 2017 (1): 80-85 
[13] Zhao Z. A novel recommendation system in location-based social networks using distributed ELM. Memetic computing, 2018, 10 (3).
[14] Wang Yue, Huang Jun, Zheng Xiaonan, et al. An improved Hybrid Recommendation Algorithm Based on User Interest and Rating Differences. Computer Engineering and Design, 2021.

Subscription
E-Mail Alert
Most popular papers
Call for Special Issue
Publication Ethics & OA Statement
Plagiarism Policy
Contact Us
Downloads: 14169
Visits: 263259

Sponsors, Associates, and Links

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.