Location-based social networks (LBSNs) allow users to share the locations that they have visited with others in a number of ways. LBSNs like Foursquare allow users to ‘check in’ to a location to share their locations with their friends. However, in Yelp, users can engage with the LBSN via modes other than check-ins. Specifically, Yelp allows users to write ‘tips’ and ‘reviews’ for the locations that they have visited. The geo-social correlations in LBSNs have been exploited to build systems that can recommend new locations to users. Traditionally, recommendation systems for LBSNs have leveraged check-ins to generate location recommendations. We demonstrate the impact of two new modalities - tips and reviews, on location recommendation. We propose a graph based recommendation framework which reconciles the ‘tip’ and ‘review’ space in Yelp in a complementary fashion. In the process, we define novel intra-user and intra-location links leveraging tip and review information, leading to a 15% increase in precision over the existing approaches.