Geo-ALM: POI Recommendation by Fusing Geographical Information and Adversarial Learning Mechanism

Geo-ALM: POI Recommendation by Fusing Geographical Information and Adversarial Learning Mechanism

Wei Liu, Zhi-Jie Wang, Bin Yao, Jian Yin

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Main track. Pages 1807-1813. https://doi.org/10.24963/ijcai.2019/250

Learning user’s preference from check-in data is important for POI recommendation. Yet, a user usually has visited some POIs while most of POIs are unvisited (i.e., negative samples). To leverage these “no-behavior” POIs, a typical approach is pairwise ranking, which constructs ranking pairs for the user and POIs. Although this approach is generally effective, the negative samples in ranking pairs are obtained randomly, which may fail to leverage “critical” negative samples in the model training. On the other hand, previous studies also utilized geographical feature to improve the recommendation quality. Nevertheless, most of previous works did not exploit geographical information comprehensively, which may also affect the performance. To alleviate these issues, we propose a geographical information based adversarial learning model (Geo-ALM), which can be viewed as a fusion of geographic features and generative adversarial networks. Its core idea is to learn the discriminator and generator interactively, by exploiting two granularity of geographic features (i.e., region and POI features). Experimental results show that Geo- ALM can achieve competitive performance, compared to several state-of-the-arts.
Keywords:
Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
Machine Learning: Feature Selection ; Learning Sparse Models
Multidisciplinary Topics and Applications: Databases
Machine Learning: Deep Learning
Machine Learning: Probabilistic Machine Learning
Machine Learning: Recommender Systems
Multidisciplinary Topics and Applications: Transportation