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Entropy 2019, 21(2), 205; https://doi.org/10.3390/e21020205

Matching Users’ Preference under Target Revenue Constraints in Data Recommendation Systems

1
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
2
School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
*
Authors to whom correspondence should be addressed.
Received: 30 January 2019 / Revised: 18 February 2019 / Accepted: 18 February 2019 / Published: 21 February 2019
(This article belongs to the Special Issue Entropy and Information in Networks, from Societies to Cities)
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Abstract

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. View Full-Text
Keywords: data recommendation; optimal recommendation distribution; utility distribution; message importance measure; importance coefficient data recommendation; optimal recommendation distribution; utility distribution; message importance measure; importance coefficient
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Liu, S.; Dong, Y.; Fan, P.; She, R.; Wan, S. Matching Users’ Preference under Target Revenue Constraints in Data Recommendation Systems. Entropy 2019, 21, 205.

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