Doing More with Less: Overcoming Data Scarcity for POI Recommendation via Cross-Region Transfer

Abstract

Variability in social app usage across regions results in a high skew of the quantity and the quality of check-in data collected, which in turn is a challenge for effective location recommender systems. In this paper, we present Axolotl (Automated cross Location-network Transfer Learning), a novel method aimed at transferring location preference models learned in a data-rich region to significantly boost the quality of recommendations in a data-scarce region. Axolotl predominantly deploys two channels for information transfer, (1) a meta-learning based procedure learned using location recommendation as well as social predictions, and (2) a lightweight unsupervised cluster-based transfer across users and locations with similar preferences. Both of these work together synergistically to achieve improved accuracy of recommendations in data-scarce regions without any prerequisite of overlapping users and with minimal fine-tuning. We build Axolotl on top of a twin graph-attention neural network model used for capturing the user- and location-conditioned influences in a user-mobility graph for each region. We conduct extensive experiments on 12 user mobility datasets across the U.S., Japan, and Germany, using 3 as source regions and 9 of them (that have much sparsely recorded mobility data) as target regions. Empirically, we show that Axolotl achieves up to 18% better recommendation performance than the existing state-of-the-art methods across all metrics.

ACM TIST

References in corpus (6)

• Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer
• Self-Attention Graph Pooling
• Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation
• DDTCDR: Deep Dual Transfer Cross Domain Recommendation
• MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation
• Region Invariant Normalizing Flows for Mobility Transfer

Cited by in corpus (2)

• Modeling Continuous Time Sequences with Intermittent Observations using Marked Temporal Point Processes
• CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Community