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Frontiers of Computer Science, DOI: 10.1007/s11704-016-5501-y Mining coterie patterns from Instagram photo trajectories for recommending popular travel routes Yaxin YU, Yuhai ZHAO, Ge YU, Guoren WANG Frontiers of Computer Science, DOI: 10.1007/s11704-016-5501-y

Problems & Ideas Problems of mining trajectory patterns from UGC data Location points of trajectories are sparse and time intervals are irregular Group-like patterns with common skeleton paths are ignored Recommendation strategies of popular tour routes for asynchronous group travelling are not addressed Ideas: discovery of coterie patterns from Instagram photo trajectories for recommending popular travel routes A novel concept of “coterie” is proposed Closed coterie patterns are discovered based on our proposed Cluster-Growth algorithm Distance-aware and Conformity-aware recommendation strategies are applied on closed coteries to recommend popular tour routes

Main Contributions A popular path from Perth to New York is recommended on DRS for visitors across from Australia and USA due to two cities’ international pivoting transportation role. More the number of trajectories is, more domestic travel routes are recommended. The popular routes in CRS shows more detailed regions than those in DRS due to the considerations of users prefers. As the value of k increasing, more detailed and personalized routes are recommended. The performance of ClusterGrowth outperforms ObjectGrowth due to ClusterGrowth's fewer number of cluster combinations.