Location Based SocialNetworks (LBSN) benefit the users by allowing them to share their locations and life
moments with their friends. The users can also review the locations they have visited. Classical recommender
systems provide users a ranked list of single items. This is not suitable for applications like trip
planning,where the recommendations should contain multiple items in an appropriate sequence. The
problem of generating such recommendations is challenging due to various critical aspects, which includes
user interest, budget constraints and high sparsity in the available data used to solve the problem.
In this paper, we propose a graph based approach to recommend a set of personalized travel packages.
Each recommended package comprises of a sequence of multiple Point of Interests (POIs). Given the current
location and spatio-temporal constraints, our goal is to recommend a package which satisfies the
constraints. This approach utilizes the data collected fromLBSNs to learn user preferences and also models
the location popularity
