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

• URL: http://arxiv.org/abs/2201.06095v1
• Date: Sun, 16 Jan 2022 17:12:52 GMT
• Title: Doing More with Less: Overcoming Data Scarcity for POI Recommendation via Cross-Region Transfer
• Authors: Vinayak Gupta and Srikanta Bedathur
• Abstract summary: Axolotl is a novel method aimed at transferring location preference models learned in a data-rich region to boost the quality of recommendations in a data-scarce region. We show that Axolotl achieves up to 18% better recommendation performance than the existing state-of-the-art methods across all metrics.
• Score: 9.571588145356277
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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