Efficient Model-Agnostic Continual Learning for Next POI Recommendation

TL;DR

This paper introduces GIRAM, a model-agnostic framework for continual next POI recommendation that adapts to evolving user interests efficiently, outperforming existing methods in accuracy, update speed, and memory usage.

Contribution

GIRAM is a novel, efficient, and model-agnostic framework that enables continual learning in next POI recommendation by integrating interest memory, key encoding, and generative retrieval modules.

Findings

GIRAM outperforms state-of-the-art methods on real-world datasets.

GIRAM maintains high efficiency in update time and memory consumption.

GIRAM effectively captures shifting user interests over time.

Abstract

Next point-of-interest (POI) recommendation improves personalized location-based services by predicting users' next destinations based on their historical check-ins. However, most existing methods rely on static datasets and fixed models, limiting their ability to adapt to changes in user behavior over time. To address this limitation, we explore a novel task termed continual next POI recommendation, where models dynamically adapt to evolving user interests through continual updates. This task is particularly challenging, as it requires capturing shifting user behaviors while retaining previously learned knowledge. Moreover, it is essential to ensure efficiency in update time and memory usage for real-world deployment. To this end, we propose GIRAM (Generative Key-based Interest Retrieval and Adaptive Modeling), an efficient, model-agnostic framework that integrates context-aware sustained interests with recent interests. GIRAM comprises four components: (1) an interest memory to preserve historical preferences; (2) a context-aware key encoding module for unified interest key representation; (3) a generative key-based retrieval module to identify diverse and relevant sustained interests; and (4) an adaptive interest update and fusion module to update the interest memory and balance sustained and recent interests. In particular, GIRAM can be seamlessly integrated with existing next POI recommendation models. Experiments on three real-world datasets demonstrate that GIRAM consistently outperforms state-of-the-art methods while maintaining high efficiency in both update time and memory consumption.