Joint optimization of recommendation and caching based on user preference prediction
Abstract The development of the Internet of things brings exponential growth of wireless traffic, which puts great pressure on the backhaul link. The proactive caching of some contents in the edge device of mobile network can effectively reduce the repeated transmission of the same contents and reli...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-07-01
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| Series: | IET Communications |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cmu2.12627 |
| _version_ | 1827572700623667200 |
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| author | Xiaoqi Chen Qi Zhu Yu Hua |
| author_facet | Xiaoqi Chen Qi Zhu Yu Hua |
| author_sort | Xiaoqi Chen |
| collection | DOAJ |
| description | Abstract The development of the Internet of things brings exponential growth of wireless traffic, which puts great pressure on the backhaul link. The proactive caching of some contents in the edge device of mobile network can effectively reduce the repeated transmission of the same contents and relieve the burden of the backhaul link. Moreover, the introduction of recommendation mechanisms can reshape user's request and improve cache hit ratio. However, the optimization of recommendation and caching decisions is highly dependent on the users’ preference information for files. Here, a joint optimization algorithm of recommendation and caching based on users’ preference prediction with multiple base stations cooperative caching is proposed. To improve the caching efficiency, the Deep Crossing model is adopted to predict users’ preferences. Under the constraints of cache capacity, recommendation quantity and bandwidth, an optimization problem to minimize the total transmission delay of the system is formulated. Then, the NP‐hardness of the proposed optimization problem is proved and it is decoupled it into three sub‐problems, namely recommendation, user access and caching optimization sub‐problems. Simulation results show that the proposed algorithm can effectively reduce the total transmission delay of the system. |
| first_indexed | 2024-03-07T19:21:20Z |
| format | Article |
| id | doaj.art-c7fc92c5a7bb46a18d114cb9c7fd3d5f |
| institution | Directory Open Access Journal |
| issn | 1751-8628 1751-8636 |
| language | English |
| last_indexed | 2024-03-07T19:21:20Z |
| publishDate | 2023-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Communications |
| spelling | doaj.art-c7fc92c5a7bb46a18d114cb9c7fd3d5f2024-02-29T11:20:44ZengWileyIET Communications1751-86281751-86362023-07-0117121335135310.1049/cmu2.12627Joint optimization of recommendation and caching based on user preference predictionXiaoqi Chen0Qi Zhu1Yu Hua2School of Communication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing ChinaSchool of Communication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing ChinaSchool of Communication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing ChinaAbstract The development of the Internet of things brings exponential growth of wireless traffic, which puts great pressure on the backhaul link. The proactive caching of some contents in the edge device of mobile network can effectively reduce the repeated transmission of the same contents and relieve the burden of the backhaul link. Moreover, the introduction of recommendation mechanisms can reshape user's request and improve cache hit ratio. However, the optimization of recommendation and caching decisions is highly dependent on the users’ preference information for files. Here, a joint optimization algorithm of recommendation and caching based on users’ preference prediction with multiple base stations cooperative caching is proposed. To improve the caching efficiency, the Deep Crossing model is adopted to predict users’ preferences. Under the constraints of cache capacity, recommendation quantity and bandwidth, an optimization problem to minimize the total transmission delay of the system is formulated. Then, the NP‐hardness of the proposed optimization problem is proved and it is decoupled it into three sub‐problems, namely recommendation, user access and caching optimization sub‐problems. Simulation results show that the proposed algorithm can effectively reduce the total transmission delay of the system.https://doi.org/10.1049/cmu2.12627access controlcache storagecooperative communication |
| spellingShingle | Xiaoqi Chen Qi Zhu Yu Hua Joint optimization of recommendation and caching based on user preference prediction IET Communications access control cache storage cooperative communication |
| title | Joint optimization of recommendation and caching based on user preference prediction |
| title_full | Joint optimization of recommendation and caching based on user preference prediction |
| title_fullStr | Joint optimization of recommendation and caching based on user preference prediction |
| title_full_unstemmed | Joint optimization of recommendation and caching based on user preference prediction |
| title_short | Joint optimization of recommendation and caching based on user preference prediction |
| title_sort | joint optimization of recommendation and caching based on user preference prediction |
| topic | access control cache storage cooperative communication |
| url | https://doi.org/10.1049/cmu2.12627 |
| work_keys_str_mv | AT xiaoqichen jointoptimizationofrecommendationandcachingbasedonuserpreferenceprediction AT qizhu jointoptimizationofrecommendationandcachingbasedonuserpreferenceprediction AT yuhua jointoptimizationofrecommendationandcachingbasedonuserpreferenceprediction |