Enhanced Collaborative Filtering for Personalized E-Government Recommendation
The problems with the information overload of e-government websites have been a big obstacle for users to make decisions. One promising approach to solve this problem is to deploy an intelligent recommendation system on e-government platforms. Collaborative filtering (CF) has shown its superiority b...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/11/24/12119 |
| _version_ | 1797506722796929024 |
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| author | Ninghua Sun Tao Chen Wenshan Guo Longya Ran |
| author_facet | Ninghua Sun Tao Chen Wenshan Guo Longya Ran |
| author_sort | Ninghua Sun |
| collection | DOAJ |
| description | The problems with the information overload of e-government websites have been a big obstacle for users to make decisions. One promising approach to solve this problem is to deploy an intelligent recommendation system on e-government platforms. Collaborative filtering (CF) has shown its superiority by characterizing both items and users by the latent features inferred from the user–item interaction matrix. A fundamental challenge is to enhance the expression of the user or/and item embedding latent features from the implicit feedback. This problem negatively affected the performance of the recommendation system in e-government. In this paper, we firstly propose to learn positive items’ latent features by leveraging both the negative item information and the original embedding features. We present the negative items mixed collaborative filtering (NMCF) method to enhance the CF-based recommender system. Such mixing information is beneficial for extending the expressiveness of the latent features. Comprehensive experimentation on a real-world e-government dataset showed that our approach improved the performance significantly compared with the state-of-the-art baseline algorithms. |
| first_indexed | 2024-03-10T04:36:43Z |
| format | Article |
| id | doaj.art-7b56f1c307db4fcd8e52de4ef6675b73 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T04:36:43Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-7b56f1c307db4fcd8e52de4ef6675b732023-11-23T03:43:29ZengMDPI AGApplied Sciences2076-34172021-12-0111241211910.3390/app112412119Enhanced Collaborative Filtering for Personalized E-Government RecommendationNinghua Sun0Tao Chen1Wenshan Guo2Longya Ran3School of Public Administration, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Public Administration, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Public Administration, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Public Administration, Huazhong University of Science and Technology, Wuhan 430074, ChinaThe problems with the information overload of e-government websites have been a big obstacle for users to make decisions. One promising approach to solve this problem is to deploy an intelligent recommendation system on e-government platforms. Collaborative filtering (CF) has shown its superiority by characterizing both items and users by the latent features inferred from the user–item interaction matrix. A fundamental challenge is to enhance the expression of the user or/and item embedding latent features from the implicit feedback. This problem negatively affected the performance of the recommendation system in e-government. In this paper, we firstly propose to learn positive items’ latent features by leveraging both the negative item information and the original embedding features. We present the negative items mixed collaborative filtering (NMCF) method to enhance the CF-based recommender system. Such mixing information is beneficial for extending the expressiveness of the latent features. Comprehensive experimentation on a real-world e-government dataset showed that our approach improved the performance significantly compared with the state-of-the-art baseline algorithms.https://www.mdpi.com/2076-3417/11/24/12119e-government public servicescollaborative filteringrecommender systemnegative sampling |
| spellingShingle | Ninghua Sun Tao Chen Wenshan Guo Longya Ran Enhanced Collaborative Filtering for Personalized E-Government Recommendation Applied Sciences e-government public services collaborative filtering recommender system negative sampling |
| title | Enhanced Collaborative Filtering for Personalized E-Government Recommendation |
| title_full | Enhanced Collaborative Filtering for Personalized E-Government Recommendation |
| title_fullStr | Enhanced Collaborative Filtering for Personalized E-Government Recommendation |
| title_full_unstemmed | Enhanced Collaborative Filtering for Personalized E-Government Recommendation |
| title_short | Enhanced Collaborative Filtering for Personalized E-Government Recommendation |
| title_sort | enhanced collaborative filtering for personalized e government recommendation |
| topic | e-government public services collaborative filtering recommender system negative sampling |
| url | https://www.mdpi.com/2076-3417/11/24/12119 |
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