City Matters! A Dual-Target Cross-City Sequential POI Recommendation Model

Existing sequential POI recommendation methods overlook a fact that each city exhibits distinct characteristics and totally ignore the city signature. In this study, we claim that city matters in sequential POI recommendation and fully exploring city signature can highlight the characteristics of each city and facilitate cross-city complementary learning. To this end, we consider the two-city scenario and propose a d ual-target c ross-city s equential P OI r ecommendation model (DCSPR) to achieve the purpose of complementary learning across cities. On one hand, DCSPR respectively captures geographical and cultural characteristics for each city by mining intra-city regions and intra-city functions of POIs. On the other hand, DCSPR builds a transfer channel between cities based on intra-city functions, and adopts a novel transfer strategy to transfer useful cultural characteristics across cities by mining inter-city functions of POIs. Moreover, to utilize these captured characteristics for sequential POI recommendation, DCSPR involves a new region- and function-aware network for each city to learn transition patterns from multiple views. Extensive experiments conducted on two real-world datasets with four cities demonstrate the effectiveness of DCSPR .