Benefits from Superposed Hawkes Processes
This work addresses the cold-start problem in recommendation systems, but it appears incremental as it builds on existing superposition models with specific theoretical and empirical validations.
The paper tackles the problem of improving the performance of temporal point process models by investigating superposed Hawkes processes, showing that superposition tightens the upper bound of excess risk under certain conditions and demonstrates feasibility on synthetic data and potential for solving cold-start problems in recommendation systems on real-world data.
The superposition of temporal point processes has been studied for many years, although the usefulness of such models for practical applications has not be fully developed. We investigate superposed Hawkes process as an important class of such models, with properties studied in the framework of least squares estimation. The superposition of Hawkes processes is demonstrated to be beneficial for tightening the upper bound of excess risk under certain conditions, and we show the feasibility of the benefit in typical situations. The usefulness of superposed Hawkes processes is verified on synthetic data, and its potential to solve the cold-start problem of recommendation systems is demonstrated on real-world data.