A hierarchical multi-agent architecture based on virtual identities to explain black-box personalization policies

Abstract

Hyper-personalization policies entail a considerable improvement regarding previous personalization approaches. However, they present several issues that need to be addressed, such as minimal explainability and privacy invasion. A hierarchical Multi-Agent System (MAS) is presented in this work to provide a solution to these concerns. The system is formulated as a hybrid approach, where some of the agents work autonomously, while the user input triggers the remaining. At the autonomous level, a set of Virtual Identities (VIs) representing different user profiles interact with Black-Box Hyper-Personalization Online Systems (BBHOS), gathering a set of targeted responses. Associative patterns and profile aggregations can then be inferred from the analysis of these responses. In the user-triggered level, the real user is virtualized as an identity that represents their features. The virtual identity serves as an intermediary between the personalization system and the real user. This virtualization hinders the personalization service from extracting sensitive contextual information about the real user, protecting their privacy. The results obtained by the user identity on its interaction with the personalization service are then analyzed, adjusting the content of the response to fit the user’s requests instead of their features. A use case on the functioning of the analysis of search engines is presented to illustrate the complete behavior of the proposed architecture.