Mobile Social Recommendation Model Integrating Users’ Personality Traits and Relationship Strength under Privacy Concerns

Abstract

Aiming at the problem of data sparsity, cold start, and privacy concerns in complex information recommendation systems, such as personalized marketing on Alibaba or TikTok, this paper proposes a mobile social recommendation model integrating users’ personality traits and social relationship strength under privacy concerns (PC-MSPR). Firstly, PC-MSPR focuses on specific personality traits, including openness, extraversion, and agreeableness, and their impacts on mobile users’ online behaviors. A personality traits calculation method that incorporates privacy preferences (PP-PTM) is then introduced. Secondly, a novel method for calculating the users’ relationship strength, based on their social network interactive activities and domain ontologies (AI-URS) is proposed. AI-URS divides the interactive activities into activity domains and calculates the strength of relationships between users belonging to the same activity domain; at the same time, the comprehensive relationship strength of users in the same domain, including direct relationships and indirect relationships, is calculated based on interactive activity documents. Finally, social recommendations are derived by integrating personality traits and social relationships to calculate user similarity. The proposed model is validated using empirical data. The results show the model’s superiority in alleviating data sparsity and cold-start problems, obtaining higher recommendation precision, and reducing the impact of privacy concerns regarding the users’ adoption of personalized recommendation services.