THE popularity of location-aware devices such as smart phones makes users freely share their activities through various location-based social networks (LBSNs), such as Foursquare and Yelp. A large amount of user-contributed data enable to develop effective point-of-interest (POI) recommender systems. It not only guides users to explore more interesting attractions, but also helps the location service providers deliver targeted advertising. Now most of existing studies focus on recommending POIs in the same city or region, named as traditional POI recommender systems. However, they fail to deal with the increasingly popular case: users travel to new cities to explore more attractions. This raises the problem that how we shall recommend POIs in a target city to a new visitor based on her/his check-in records in source cities. We refer to this problem as crossing-city POI recommendations. Compared with traditional POI recommender systems, crossing-city POI recommender systems are more challenging due to the following aspects: