  POI recommendation is a key task in tourism information systems. However, in
contrast to conventional point of interest (POI) recommender systems, the
available data is extremely sparse; most tourist visit a few sightseeing spots
once and most of these spots have no check-in data from new tourists. Most
conventional systems rank sightseeing spots based on their popularity,
reputations, and category-based similarities with users' preferences. They do
not clarify what users can experience in these spots, which makes it difficult
to meet diverse tourism needs. To this end, in this work, we propose a
mechanism to recommend POIs to tourists. Our mechanism include two components:
one is a probabilistic model that reveals the user behaviors in tourism; the
other is a pseudo rating mechanism to handle the cold-start issue in POIs
recommendations. We carried out extensive experiments with two datasets
collected from Flickr. The experimental results demonstrate that our methods
are superior to the state-of-the-art methods in both the recommendation
performances (precision, recall and F-measure) and fairness. The experimental
results also validate the robustness of the proposed methods, i.e., our methods
can handle well the issue of data sparsity.
