Region Invariant Normalizing Flows for Mobility Transfer

TL;DR

This paper introduces REFORMD, a transfer learning framework using normalizing flows for accurate location prediction in regions with sparse data, significantly improving mobility modeling and recommendation tasks.

Contribution

The paper presents a novel transfer learning approach with normalizing flows for region-invariant mobility prediction, handling data scarcity and evolving check-in dynamics.

Findings

Outperforms state-of-the-art methods in mobility prediction

Effectively transfers models across regions with different data volumes

Adaptable for product recommendation without spatial data

Abstract

There exists a high variability in mobility data volumes across different regions, which deteriorates the performance of spatial recommender systems that rely on region-specific data. In this paper, we propose a novel transfer learning framework called REFORMD, for continuous-time location prediction for regions with sparse checkin data. Specifically, we model user-specific checkin-sequences in a region using a marked temporal point process (MTPP) with normalizing flows to learn the inter-checkin time and geo-distributions. Later, we transfer the model parameters of spatial and temporal flows trained on a data-rich origin region for the next check-in and time prediction in a target region with scarce checkin data. We capture the evolving region-specific checkin dynamics for MTPP and spatial-temporal flows by maximizing the joint likelihood of next checkin with three channels (1) checkin-category prediction, (2) checkin-time prediction, and (3) travel distance prediction. Extensive experiments on different user mobility datasets across the U.S. and Japan show that our model significantly outperforms state-of-the-art methods for modeling continuous-time sequences. Moreover, we also show that REFORMD can be easily adapted for product recommendations i.e., sequences without any spatial component.