Learning from Hometown and Current City: Cross-city POI Recommendation via Interest Drift and Transfer Learning

With more and more frequent population movement between different cities, like users' travel or business trip, recommending personalized cross-city Point-of-Interests (POIs) for these users has become an important scenario of POI recommendation tasks. However, traditional models degrade significantly due to sparsity problem because travelers only have limited visiting behaviors. Through a detailed analysis of real-world check-data, we observe 1) the phenomenon of travelers' interest drift and transfer co-exist between hometown and current city; 2) differences between popular POIs among locals and travelers. Motivated by this, we propose a POI Recommendation framework with User Interest Drift and Transfer (PR-UIDT), which jointly considers above two factors when designing user and POI latent vector. In this framework, user vector is divided into a city-independent part and another city-dependent part, and POI is represented as two independent vectors for locals and travelers, respectively. To evaluate the proposed framework, we implement it with a square error based matrix factorization model and a ranking error based matrix factorization model, respectively, and conduct extensive experiments on three real-world datasets. The experiment results demonstrate the superiority of PR-UIDT framework, with a relative improvement of 0.4% 20.5% over several state-of-the-art baselines, as well as the practicality of applying this framework to real-world applications and multi-city scenarios. Further qualitative analysis confirms both the plausibility and validity of combining user interest transfer and drift into cross-city POI recommendation.