Nguyen Duc Khang Quach

This study explores how phygital profiling and dynamic personas enhance personalized customer experiences across the evolving customer journey. Drawing on Foursquare data from 27,148 users and over 2 million check-ins across four global cities, we construct digital twins using location-based social network graphs. We compare the effectiveness of phygital versus digital profiling and dynamic versus static personas in two key tasks: point-of-interest (POI) recommendation and social connection mapping. Results show that phygital profiling significantly outperforms digital profiling, and dynamic personas yield more accurate, real-time personalization than static approaches. These findings underscore the value of integrating physical and digital data in capturing the complexity of modern consumer behavior. The study offers theoretical and practical insights for advancing adaptive personalization and customer engagement strategies in increasingly hybrid consumption environments.

With recent advancements in graph neural networks (GNN), GNN-based recommender systems (gRS) have achieved remarkable success in the past few years. Despite this success, existing research reveals that gRSs are still vulnerable to poison attacks , in which the attackers inject fake data to manipulate recommendation results as they desire. This might be due to the fact that existing poison attacks (and countermeasures) are either model-agnostic or specifically designed for traditional recommender algorithms (e.g., neighborhood-based, matrix-factorization-based, or deep-learning-based RSs) that are not gRS. As gRSs are widely adopted in the industry, the problem of how to design poison attacks for gRSs has become a need for robust user experience. Herein, we focus on the use of poison attacks to manipulate item promotion in gRSs. Compared to standard GNNs, attacking gRSs is more challenging due to the heterogeneity of network structure and the entanglement between users and items. To overcome such challenges, we propose GSPAttack —a generative surrogate-based poison attack framework for gRSs. GSPAttack tailors a learning process to surrogate a recommendation model as well as generate fake users and user-item interactions while preserving the data correlation between users and items for recommendation accuracy. Although maintaining high accuracy for other items rather than the target item seems counterintuitive, it is equally crucial to the success of a poison attack. Extensive evaluations on four real-world datasets revealed that GSPAttack outperforms all baselines with competent recommendation performance and is resistant to various countermeasures.