Geo-ALM: POI Recommendation by Fusing Geographical Information and
Adversarial Learning Mechanism
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 GeoALM can achieve competitive performance, compared to several state-of-the-arts