Curriculum Meta-Learning for Next POI Recommendation

Abstract

Next point-of-interest (POI) recommendation is a hot research field where a recent emerging scenario, next POI to search recommendation, has been deployed in many online map services such as Baidu Maps. One of the key issues in this scenario is providing satisfactory recommendation services for cold-start cities with a limited number of user-POI interactions, which requires transferring the knowledge hidden in rich data from many other cities to these cold-start cities. Existing literature either does not consider the city-transfer issue or cannot simultaneously tackle the data sparsity and pattern diversity issues among various users in multiple cities. To address these issues, we explore city-transfer next POI to search recommendation that transfers the knowledge from multiple cities with rich data to cold-start cities with scarce data. We propose a novel Curriculum Hardness Aware Meta-Learning (CHAML) framework, which incorporates hard sample mining and curriculum learning into a meta-learning paradigm. Concretely, the CHAML framework considers both city-level and user-level hardness to enhance the conditional sampling during meta training, and uses an easy-to-hard curriculum for the city-sampling pool to help the meta-learner converge to a better state. Extensive experiments on two real-world map search datasets from Baidu Maps demonstrate the superiority of CHAML framework.