##IPython notebooks

• rank_markov.ipynb IPython notebook to recommend trajectories using methods described in paper.
• parse_results.ipynb IPython notebook to generate performance tables (Table 3 and Table 4 in paper) using dumped results.

##Dataset

• data/poi-Edin.csv POI data in Edinburgh.
  • poiID POI identity
  • poiCat POI category
  • poiLon POI longitude
  • poiLat POI latitude
• data/traj-Edin.csv Trajectories in Edinburgh.
  • userID User identity
  • trajID Trajectory identity
  • poiID POI identity
  • startTime Timestamp that the user started to visit this POI
  • endTime Timestamp that the user left this POI
  • #photo Number of photos taken by the user at this POI
  • trajLen Number of POIs visited in this trajectory by the user
  • poiDuration The visit duration (seconds) at this POI by the user
• data/poi-Glas.csv POI data in Glasgow.
• data/traj-Glas.csv Trajectories in Glasgow.
• data/poi-Melb.csv POI data in Melbourne.
• data/traj-Melb.csv Trajectories in Melbourne.
• data/poi-Osak.csv POI data in Osaka.
• data/traj-Osak.csv Trajectories in Osaka.
• data/poi-Toro.csv POI data in Toronto.
• data/traj-Toro.csv Trajectories in Toronto.
• data/rand-*.pkl Dumped recommendations by method Random described in paper.
• data/rank-*.pkl Dumped recommendations by methods PoiPopularity and PoiRank described in paper.
• data/tran-*.pkl Dumped recommendations by methods Markov and MarkovPath described in paper.
• data/comb-*.pkl Dumped recommendations by methods Rank+Markov and Rank+MarkovPath described in paper.
• data/ijcai-*.pkl Dumped recommendations by method PersTour proposed in this paper and its variant PersTour-L.

##Usage To generate recommendations from scratch, please follow these four steps:

1. Install rankSVM implementations and assign the directory/path to variable ranksvm_dir in notebook rank_markov.ipynb.
2. Install Python modules imported in notebook rank_markov.ipynb.
3. Modify the value of dataset index variable dat_ix (feasible values: 0, 1, 2, 3, 4) to run notebook rank_markov.ipynb on different dataset, results (.pkl file) will be saved in the directory specified by variable data_dir.
4. After running notebook rank_markov.ipynb on all 5 datasets, please run notebook parse_results.ipynb to generate Table 3 and Table 4 in paper.

##Citation Please cite these two papers if you use this dataset in your work.

• Kwan Hui Lim, Jeffrey Chan, Christopher Leckie and Shanika Karunasekera. "Personalized Tour Recommendation based on User Interests and Points of Interest Visit Durations". In Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI'15). 2015.
• Dawei Chen, Cheng Soon Ong and Lexing Xie. "Learning Points and Routes to Recommend Trajectories". In Proceedings of the 25th ACM International Conference on Information and Knowledge Management (CIKM'16). 2016.