Weighted meta-graph based mobile application recommendation through matrix factorisation and neural networks
Numerous mobile applications (apps) with different functions meet the various needs of users, but users have to spend a lot of time selecting suitable mobile apps. How to select relevant mobile apps for users has become an important issue. Existing studies mainly utilise context, user interest, priv...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
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| Series: | Connection Science |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/09540091.2023.2289834 |
| _version_ | 1797371637449883648 |
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| author | Fenfang Xie Angyu Zheng Liang Chen Zibin Zheng Mingdong Tang |
| author_facet | Fenfang Xie Angyu Zheng Liang Chen Zibin Zheng Mingdong Tang |
| author_sort | Fenfang Xie |
| collection | DOAJ |
| description | Numerous mobile applications (apps) with different functions meet the various needs of users, but users have to spend a lot of time selecting suitable mobile apps. How to select relevant mobile apps for users has become an important issue. Existing studies mainly utilise context, user interest, privacy, security, version, and heterogeneous information to make mobile app recommendations. However, they have at least one of the following limitations: (1) Don't fully integrate the rich heterogeneous information; (2) Don't capture complex structural and semantic information; (3) Don't differentiate the importance of different semantic meta-graphs; (4) Don't consider the influence of different users' rating criteria. Therefore, the predictive performance of these methods is relatively limited. This paper considers the influence of different users' rating criteria for the same app and proposes a weighted meta-graph based mobile app recommendation approach by leveraging matrix factorisation and neural networks. Specifically, the similarity measurement between users and apps considers the difference in users' rating criteria under various semantic meta-graph patterns. The matrix factorisation technology is used to acquire the user's and the app's latent feature matrices. The importance of various semantic meta-graphs is distinguished by exploiting the weight learning. The neural network technology is employed to learn interactions between users and apps, thereby predicting the user's preference for unobserved apps. Experimental results demonstrate the superiority of the proposed approach, the effectiveness of considering differences in users' rating criteria, and the importance of differentiating various semantic meta-graphs. |
| first_indexed | 2024-03-08T18:22:28Z |
| format | Article |
| id | doaj.art-81f25febd8884e22be9f59bdae626e7e |
| institution | Directory Open Access Journal |
| issn | 0954-0091 1360-0494 |
| language | English |
| last_indexed | 2024-03-08T18:22:28Z |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj.art-81f25febd8884e22be9f59bdae626e7e2023-12-30T18:02:26ZengTaylor & Francis GroupConnection Science0954-00911360-04942024-12-0136110.1080/09540091.2023.2289834Weighted meta-graph based mobile application recommendation through matrix factorisation and neural networksFenfang Xie0Angyu Zheng1Liang Chen2Zibin Zheng3Mingdong Tang4School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou, People's Republic of ChinaSchool of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, People's Republic of ChinaSchool of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, People's Republic of ChinaSchool of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, People's Republic of ChinaSchool of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou, People's Republic of ChinaNumerous mobile applications (apps) with different functions meet the various needs of users, but users have to spend a lot of time selecting suitable mobile apps. How to select relevant mobile apps for users has become an important issue. Existing studies mainly utilise context, user interest, privacy, security, version, and heterogeneous information to make mobile app recommendations. However, they have at least one of the following limitations: (1) Don't fully integrate the rich heterogeneous information; (2) Don't capture complex structural and semantic information; (3) Don't differentiate the importance of different semantic meta-graphs; (4) Don't consider the influence of different users' rating criteria. Therefore, the predictive performance of these methods is relatively limited. This paper considers the influence of different users' rating criteria for the same app and proposes a weighted meta-graph based mobile app recommendation approach by leveraging matrix factorisation and neural networks. Specifically, the similarity measurement between users and apps considers the difference in users' rating criteria under various semantic meta-graph patterns. The matrix factorisation technology is used to acquire the user's and the app's latent feature matrices. The importance of various semantic meta-graphs is distinguished by exploiting the weight learning. The neural network technology is employed to learn interactions between users and apps, thereby predicting the user's preference for unobserved apps. Experimental results demonstrate the superiority of the proposed approach, the effectiveness of considering differences in users' rating criteria, and the importance of differentiating various semantic meta-graphs.https://www.tandfonline.com/doi/10.1080/09540091.2023.2289834Mobile app recommendationheterogeneous information networkmeta-graphmatrix factorisationneural network |
| spellingShingle | Fenfang Xie Angyu Zheng Liang Chen Zibin Zheng Mingdong Tang Weighted meta-graph based mobile application recommendation through matrix factorisation and neural networks Connection Science Mobile app recommendation heterogeneous information network meta-graph matrix factorisation neural network |
| title | Weighted meta-graph based mobile application recommendation through matrix factorisation and neural networks |
| title_full | Weighted meta-graph based mobile application recommendation through matrix factorisation and neural networks |
| title_fullStr | Weighted meta-graph based mobile application recommendation through matrix factorisation and neural networks |
| title_full_unstemmed | Weighted meta-graph based mobile application recommendation through matrix factorisation and neural networks |
| title_short | Weighted meta-graph based mobile application recommendation through matrix factorisation and neural networks |
| title_sort | weighted meta graph based mobile application recommendation through matrix factorisation and neural networks |
| topic | Mobile app recommendation heterogeneous information network meta-graph matrix factorisation neural network |
| url | https://www.tandfonline.com/doi/10.1080/09540091.2023.2289834 |
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