Online Continuous-Time Tensor Factorization Based on Pairwise Interactive Point Processes

Hongteng Xu, Dixin Luo, Lawrence Carin
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
A continuous-time tensor factorization method is developed for event sequences containing multiple "modalities." Each data element is a point in a tensor, whose dimensions are associated with the discrete alphabet of the modalities. Each tensor data element has an associated time of occurence and a feature vector. We model such data based on pairwise interactive point processes, and the proposed framework connects pairwise tensor factorization with a feature-embedded point process. The model
more » ... ounts for interactions within each modality, interactions across different modalities, and continuous-time dynamics of the interactions. Model learning is formulated as a convex optimization problem, based on online alternating direction method of multipliers. Compared to existing state-of-the-art methods, our approach captures the latent structure of the tensor and its evolution over time, obtaining superior results on real-world datasets.
doi:10.24963/ijcai.2018/403 dblp:conf/ijcai/XuLC18 fatcat:3mrtbkczi5ejnchaxkk2vcwsdi