Memory-augmented meta-learning on meta-path for fast adaptation cold-start recommendation
Personalised recommendation is a difficult problem that has received a lot of attention to academia and industry. Because of the sparse user–item interaction, cold-start recommendation has been a particularly difficult problem. Some efforts have been made to solve the cold-start problem by using mod...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-12-01
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| Series: | Connection Science |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/09540091.2021.1996537 |
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| _version_ | 1850225240215388160 |
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| author | Tianyuan Li Xin Su Wei Liu Wei Liang Meng-Yen Hsieh Zhuhui Chen XuChong Liu Hong Zhang |
| author_facet | Tianyuan Li Xin Su Wei Liu Wei Liang Meng-Yen Hsieh Zhuhui Chen XuChong Liu Hong Zhang |
| author_sort | Tianyuan Li |
| collection | DOAJ |
| description | Personalised recommendation is a difficult problem that has received a lot of attention to academia and industry. Because of the sparse user–item interaction, cold-start recommendation has been a particularly difficult problem. Some efforts have been made to solve the cold-start problem by using model-agnostic meta-learning on the level of the model and heterogeneous information networks on the level of data. Moreover, using the memory-augmented meta-optimisation method effectively prevents the meta-learning model from entering the local optimum. As a result, this paper proposed memory-augmented meta-learning on meta-path, a new meta-learning method that addresses the cold-start recommendation on the meta-path furthered. The meta-path builds at the data level to enrich the relevant semantic information of the data. To achieve fast adaptation, semantic-specific memory is utilised to conduct the model with semantic parameter initialisation, and the method is optimised by a meta-optimisation method. We put this method to the test using two widely used recommended data set and three cold-start scenarios. The experimental results demonstrate the efficiency of our proposed method. |
| format | Article |
| id | doaj-art-577d8355a6064e46a1dfbaf644632e6a |
| institution | OA Journals |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-577d8355a6064e46a1dfbaf644632e6a2025-08-20T02:05:25ZengTaylor & Francis GroupConnection Science0954-00911360-04942022-12-0134130131810.1080/09540091.2021.19965371996537Memory-augmented meta-learning on meta-path for fast adaptation cold-start recommendationTianyuan Li0Xin Su1Wei Liu2Wei Liang3Meng-Yen Hsieh4Zhuhui Chen5XuChong Liu6Hong Zhang7Xiangtan UniversityHunan Provincial Key Laboratory of Network Investigational Technology, Hunan Police AcademyXiangtan UniversityCollege of Computer Science and Electronic Engineering, Hunan UniversityComputer Science and Information Engineering, Providence UniversityXiangtan UniversityHunan Provincial Key Laboratory of Network Investigational Technology, Hunan Police AcademySchool of Economics and Management, Changsha University of Science and TechnologyPersonalised recommendation is a difficult problem that has received a lot of attention to academia and industry. Because of the sparse user–item interaction, cold-start recommendation has been a particularly difficult problem. Some efforts have been made to solve the cold-start problem by using model-agnostic meta-learning on the level of the model and heterogeneous information networks on the level of data. Moreover, using the memory-augmented meta-optimisation method effectively prevents the meta-learning model from entering the local optimum. As a result, this paper proposed memory-augmented meta-learning on meta-path, a new meta-learning method that addresses the cold-start recommendation on the meta-path furthered. The meta-path builds at the data level to enrich the relevant semantic information of the data. To achieve fast adaptation, semantic-specific memory is utilised to conduct the model with semantic parameter initialisation, and the method is optimised by a meta-optimisation method. We put this method to the test using two widely used recommended data set and three cold-start scenarios. The experimental results demonstrate the efficiency of our proposed method.http://dx.doi.org/10.1080/09540091.2021.1996537cold-start recommendationmemory-augmentedmeta-pathmeta-learning |
| spellingShingle | Tianyuan Li Xin Su Wei Liu Wei Liang Meng-Yen Hsieh Zhuhui Chen XuChong Liu Hong Zhang Memory-augmented meta-learning on meta-path for fast adaptation cold-start recommendation Connection Science cold-start recommendation memory-augmented meta-path meta-learning |
| title | Memory-augmented meta-learning on meta-path for fast adaptation cold-start recommendation |
| title_full | Memory-augmented meta-learning on meta-path for fast adaptation cold-start recommendation |
| title_fullStr | Memory-augmented meta-learning on meta-path for fast adaptation cold-start recommendation |
| title_full_unstemmed | Memory-augmented meta-learning on meta-path for fast adaptation cold-start recommendation |
| title_short | Memory-augmented meta-learning on meta-path for fast adaptation cold-start recommendation |
| title_sort | memory augmented meta learning on meta path for fast adaptation cold start recommendation |
| topic | cold-start recommendation memory-augmented meta-path meta-learning |
| url | http://dx.doi.org/10.1080/09540091.2021.1996537 |
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