Many types of event sequence data exhibit triggering and clustering properties in space and time. Point processes are widely used in modeling such event data with applications such as predictive policing and disaster event forecasting. Although current algorithms can achieve significant event prediction accuracy, the historic data or the self-excitation property can introduce biased prediction. For example, hotspots ranked by event hazard rates can make the visibility of a disadvantaged group (e.g., racial minorities or the communities of lower social economic status) more apparent. Existing methods have explored ways to achieve parity between the groups by penalizing the objective function with several group fairness metrics. However, these metrics fail to measure the fairness on every prefix of the ranking. In this paper, we propose a novel list-wise fairness criterion for point processes, which can efficiently evaluate the ranking fairness in event prediction. We also present a strict definition of the unfairness consistency property of a fairness metric and prove that our list-wise fairness criterion satisfies this property. Experiments on several real-world spatial-temporal sequence datasets demonstrate the effectiveness of our list-wise fairness criterion.

Hawkes processes are popular for modeling correlated temporal sequences that exhibit mutual-excitation properties. Existing approaches such as feature-enriched processes or variations of Multivariate Hawkes processes either fail to describe the exact mutual influence between sequences or become computational inhibitive in most real-world applications involving large dimensions. Incorporating additional geometric structure in the form of graphs into Hawkes processes is an effective and efficient way for improving model prediction accuracy. In this paper, we propose the Geometric Hawkes Process (GHP) model to better correlate individual processes, by integrating Hawkes processes and a graph convolutional recurrent neural network. The deep network structure is computational efficient since it requires constant parameters that are independent of the graph size. The experiment results on real-world data show that our framework outperforms recent state-of-art methods.

Hawkes processes have become very popular in modeling multiple recurrent user-item interaction events that exhibit mutual-excitation properties in various domains. Generally, modeling the interaction sequence of each user-item pair as an independent Hawkes process is ineffective since the prediction accuracy of future event occurrences for users and items with few observed interactions is low. On the other hand, multivariate Hawkes processes (MHPs) can be used to handle multi-dimensional random processes where different dimensions are correlated with each other. However, an MHP either fails to describe the correct mutual influence between dimensions or become computational inhibitive in most real-world events involving a large collection of users and items. To tackle this challenge, we propose local low-rank Hawkes processes to model large-scale user-item interactions, which efficiently captures the correlations of Hawkes processes in different dimensions. In addition, we design an efficient convex optimization algorithm to estimate model parameters and present a parallel algorithm to further increase the computation efficiency. Extensive experiments on real-world datasets demonstrate the performance improvements of our model in comparison with the state of the art.

User demographics such as age and gender are very useful in recommender systems for applications such as personalization services and marketing, but may not always be available for individual users. Existing approaches can infer users' private demographics based on ratings, given labeled data from users who share demographics. However, such labeled information is not always available in many e-commerce services, particularly small online retailers and most media sites, for which no user registration is required. We introduce a novel probabilistic matrix factorization model for demographic transfer that enables knowledge transfer from the source domain, in which users' ratings and the corresponding demographics are available, to the target domain, in which we would like to infer unknown user demographics from ratings. Our proposed method is based on two observations: (1) Items from different but related domains may share the same latent factors such as genres and styles, and (2) Users who share similar demographics are likely to prefer similar genres across domains. This approach can align latent factors across domains that share neither common users nor common items, associating user demographics with latent factors in a unified framework. Experiments on cross-domain datasets demonstrate that the proposed method consistently improves demographic classification accuracy over existing methods.