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

Abstract

This paper focuses on the problem of finding a particular data recommendation strategy based on the user preferences 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 system 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.

Storage Space Allocation Strategy for Digital Data with Message Importance

“…Consider the situation where the data is stored according to its category for easier retrieval, which can also make the recommendation system based on it more effective [ 33 ]. Since data classification is becoming increasingly convenient and accurate nowadays due to the rapid development of machine learning [ 35 , 36 ], this paper assumes that the event class can be easily detected and known in the storage system.…”

“…In practice, the values of depend on the user preferences. For instance, when is positive, the user only focuses on the small-probability events, while the large-probability events are focused on when is negative [ 33 ].…”

“…Furthermore, MIM is fairly effective in many applications of big data, such as IoT [ 31 ], mobile edge computing [ 32 ]. In addition, Reference [ 33 ] expanded MIM to the general case, and it presented that MIM can be adopted as a special weight in designing the recommendation system. Since there is no universal data value model, we might as well take the case where the MIM describes the cost of the error as a quintessential example to analyze the property of the optimal storage space allocation strategy.…”

Attention to the Variation of Probabilistic Events: Information Processing with Message Importance Measure

“…[27] has investigated the minor probability event detection by combining MIM and Bayes detection. Moreover, it is worth noting that the physical meaning of the components of MIM corresponds to the normalized optimal data recommendation distribution, which makes a trade-off between the users' preference and system revenue [28]. In this respect, MIM plays a fundamental role in the recommendation system (a popular applications of big data) from the theoretic viewpoint.…”

Storage Space Allocation Strategy for Digital Data with Message Importance

“…Besides, Ref. [32] expanded MIM to the general case, and it presented that MIM can be adopted as a special weight in designing the recommendation system. Thus, this paper will illuminate the properties of this new compression strategy with taking MIM as the importance weight.…”