Deep Learning-Based Context-Aware Recommender System Considering Change in Preference
In order to predict and recommend what users want, users’ information is required, and more information is required to improve the performance of the recommender system. As IoT devices and smartphones have made it possible to know the user’s context, context-aware recommender systems have emerged to...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-05-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/12/10/2337 |
| _version_ | 1797600369310695424 |
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| author | Soo-Yeon Jeong Young-Kuk Kim |
| author_facet | Soo-Yeon Jeong Young-Kuk Kim |
| author_sort | Soo-Yeon Jeong |
| collection | DOAJ |
| description | In order to predict and recommend what users want, users’ information is required, and more information is required to improve the performance of the recommender system. As IoT devices and smartphones have made it possible to know the user’s context, context-aware recommender systems have emerged to predict preferences by considering the user’s context. A context-aware recommender system uses contextual information such as time, weather, and location to predict preferences. However, a user’s preferences are not always the same in a given context. They may follow trends or make different choices due to changes in their personal environment. Therefore, in this paper, we propose a context-aware recommender system that considers the change in users’ preferences over time. The proposed method is a context-aware recommender system that uses Matrix Factorization with a preference transition matrix to capture and reflect the changes in users’ preferences. To evaluate the performance of the proposed method, we compared the performance with the traditional recommender system, context-aware recommender system, and dynamic recommender system, and confirmed that the performance of the proposed method is better than the existing methods. |
| first_indexed | 2024-03-11T03:47:08Z |
| format | Article |
| id | doaj.art-d1ff83de11554609a34f43e12e991d7f |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T03:47:08Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-d1ff83de11554609a34f43e12e991d7f2023-11-18T01:10:59ZengMDPI AGElectronics2079-92922023-05-011210233710.3390/electronics12102337Deep Learning-Based Context-Aware Recommender System Considering Change in PreferenceSoo-Yeon Jeong0Young-Kuk Kim1Division of Software Engineering, Pai Chai University, Daejeon 35345, Republic of KoreaDepartment of Computer Science & Engineering, Chungnam National University, Daejeon 34134, Republic of KoreaIn order to predict and recommend what users want, users’ information is required, and more information is required to improve the performance of the recommender system. As IoT devices and smartphones have made it possible to know the user’s context, context-aware recommender systems have emerged to predict preferences by considering the user’s context. A context-aware recommender system uses contextual information such as time, weather, and location to predict preferences. However, a user’s preferences are not always the same in a given context. They may follow trends or make different choices due to changes in their personal environment. Therefore, in this paper, we propose a context-aware recommender system that considers the change in users’ preferences over time. The proposed method is a context-aware recommender system that uses Matrix Factorization with a preference transition matrix to capture and reflect the changes in users’ preferences. To evaluate the performance of the proposed method, we compared the performance with the traditional recommender system, context-aware recommender system, and dynamic recommender system, and confirmed that the performance of the proposed method is better than the existing methods.https://www.mdpi.com/2079-9292/12/10/2337recommender systemscontext-awaredeep learningtransition matrix |
| spellingShingle | Soo-Yeon Jeong Young-Kuk Kim Deep Learning-Based Context-Aware Recommender System Considering Change in Preference Electronics recommender systems context-aware deep learning transition matrix |
| title | Deep Learning-Based Context-Aware Recommender System Considering Change in Preference |
| title_full | Deep Learning-Based Context-Aware Recommender System Considering Change in Preference |
| title_fullStr | Deep Learning-Based Context-Aware Recommender System Considering Change in Preference |
| title_full_unstemmed | Deep Learning-Based Context-Aware Recommender System Considering Change in Preference |
| title_short | Deep Learning-Based Context-Aware Recommender System Considering Change in Preference |
| title_sort | deep learning based context aware recommender system considering change in preference |
| topic | recommender systems context-aware deep learning transition matrix |
| url | https://www.mdpi.com/2079-9292/12/10/2337 |
| work_keys_str_mv | AT sooyeonjeong deeplearningbasedcontextawarerecommendersystemconsideringchangeinpreference AT youngkukkim deeplearningbasedcontextawarerecommendersystemconsideringchangeinpreference |