Matching Users’ Preference under Target Revenue Constraints in Data Recommendation Systems
This paper focuses on the problem of finding a particular data recommendation strategy based on the user preference and a system expected revenue. To this end, we formulate this problem as an optimization by designing the recommendation mechanism as close to the user behavior as possible with a cert...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-02-01
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| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/21/2/205 |
| _version_ | 1811299867326152704 |
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| author | Shanyun Liu Yunquan Dong Pingyi Fan Rui She Shuo Wan |
| author_facet | Shanyun Liu Yunquan Dong Pingyi Fan Rui She Shuo Wan |
| author_sort | Shanyun Liu |
| collection | DOAJ |
| description | This paper focuses on the problem of finding a particular data recommendation strategy based on the user preference and a system expected revenue. To this end, we formulate this problem as an optimization by designing the recommendation mechanism as close to the user behavior as possible with a certain revenue constraint. In fact, the optimal recommendation distribution is the one that is the closest to the utility distribution in the sense of relative entropy and satisfies expected revenue. We show that the optimal recommendation distribution follows the same form as the message importance measure (MIM) if the target revenue is reasonable, i.e., neither too small nor too large. Therefore, the optimal recommendation distribution can be regarded as the normalized MIM, where the parameter, called importance coefficient, presents the concern of the system and switches the attention of the system over data sets with different occurring probability. By adjusting the importance coefficient, our MIM based framework of data recommendation can then be applied to systems with various system requirements and data distributions. Therefore, the obtained results illustrate the physical meaning of MIM from the data recommendation perspective and validate the rationality of MIM in one aspect. |
| first_indexed | 2024-04-13T06:42:58Z |
| format | Article |
| id | doaj.art-759362f212d64bb4b276a62b8452dd7d |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-13T06:42:58Z |
| publishDate | 2019-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-759362f212d64bb4b276a62b8452dd7d2022-12-22T02:57:41ZengMDPI AGEntropy1099-43002019-02-0121220510.3390/e21020205e21020205Matching Users’ Preference under Target Revenue Constraints in Data Recommendation SystemsShanyun Liu0Yunquan Dong1Pingyi Fan2Rui She3Shuo Wan4Department of Electronic Engineering, Tsinghua University, Beijing 100084, ChinaSchool of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaDepartment of Electronic Engineering, Tsinghua University, Beijing 100084, ChinaDepartment of Electronic Engineering, Tsinghua University, Beijing 100084, ChinaDepartment of Electronic Engineering, Tsinghua University, Beijing 100084, ChinaThis paper focuses on the problem of finding a particular data recommendation strategy based on the user preference and a system expected revenue. To this end, we formulate this problem as an optimization by designing the recommendation mechanism as close to the user behavior as possible with a certain revenue constraint. In fact, the optimal recommendation distribution is the one that is the closest to the utility distribution in the sense of relative entropy and satisfies expected revenue. We show that the optimal recommendation distribution follows the same form as the message importance measure (MIM) if the target revenue is reasonable, i.e., neither too small nor too large. Therefore, the optimal recommendation distribution can be regarded as the normalized MIM, where the parameter, called importance coefficient, presents the concern of the system and switches the attention of the system over data sets with different occurring probability. By adjusting the importance coefficient, our MIM based framework of data recommendation can then be applied to systems with various system requirements and data distributions. Therefore, the obtained results illustrate the physical meaning of MIM from the data recommendation perspective and validate the rationality of MIM in one aspect.https://www.mdpi.com/1099-4300/21/2/205data recommendationoptimal recommendation distributionutility distributionmessage importance measureimportance coefficient |
| spellingShingle | Shanyun Liu Yunquan Dong Pingyi Fan Rui She Shuo Wan Matching Users’ Preference under Target Revenue Constraints in Data Recommendation Systems Entropy data recommendation optimal recommendation distribution utility distribution message importance measure importance coefficient |
| title | Matching Users’ Preference under Target Revenue Constraints in Data Recommendation Systems |
| title_full | Matching Users’ Preference under Target Revenue Constraints in Data Recommendation Systems |
| title_fullStr | Matching Users’ Preference under Target Revenue Constraints in Data Recommendation Systems |
| title_full_unstemmed | Matching Users’ Preference under Target Revenue Constraints in Data Recommendation Systems |
| title_short | Matching Users’ Preference under Target Revenue Constraints in Data Recommendation Systems |
| title_sort | matching users preference under target revenue constraints in data recommendation systems |
| topic | data recommendation optimal recommendation distribution utility distribution message importance measure importance coefficient |
| url | https://www.mdpi.com/1099-4300/21/2/205 |
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