User to Vector: Encoding User Behavior from Co-occurrence of Observations

In this study, we propose the User2Vec framework, a novel approach for capturing user behavior based on the co-occurrence of user activities. User behavior modeling is essential for understanding relationships in areas such as e-learning and marketing, but most existing methods rely on explicit social interaction data, which are not always available. We propose the User2Vec framework, inspired by the Word2Vec model, to encode user activity into dense vector representations using the temporal co-occurrence of active users. This approach allows the identification of user groups without relying on direct interaction data. The proposed methodology involves preprocessing user activity logs, extracting temporal co-occurrence features, and applying clustering techniques such as k-means and spectral clustering to reveal latent structures. These clustering methods were chosen for their balance of computational efficiency and effectiveness in capturing implicit relationships. The approach is experimented on a dataset from a university e-learning platform, where student logs are processed to cluster users according to their co-occurring actions. Comparative evaluations show that the proposed model outperforms previous co-occurrence-based methods in terms of clustering accuracy and computational efficiency. Our results highlight the significant potential of the proposed framework in applications such as personalized learning and community detection.