Abstract
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
