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Personalized travel route recommendation from multi-source social media data

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Abstract

Personalized travel route recommendation aims to recommend tourist attractions based on user’s interest and generate routes which can be viewed as a recommendation and sequence task. How to build interest model for users to find personalized attractions and generate the location sequence brings great challenges. In this work, multi-source social media (e.g., travelogues and check-in records) are leveraged to find user interests and model route attributes. In order to fuse multi-source data in a unify metric space, a topical package is built as the measurement space. Then, a long short-term memory (LSTM) based method is used to generate some candidate positions based on sparse user-specified inputs. Finally, top ranked routes are recommended as final results. The proposed approach combining multi-source social based topical package and LSTM is evaluated on a real travel dataset and compared with three state-of-the-art methods. The experimental result shows that our method performs better for providing personalized travel routes.

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References

  1. Alahi A, Goel K, Ramanathan V, Robicquet A, Li F, Savarese S (2016) Social LSTM: human trajectory prediction in crowded spaces. In: 2016 IEEE Conference on computer vision and pattern recognition, CVPR 2016, las vegas, NV, USA, June 27-30, 2016, pp 961–971

  2. Brilhante IR, de Macėdo J. A. F., Nardini FM, Perego R, Renso C (2014) Tripbuilder: a tool for recommending sightseeing tours. In: Advances in information retrieval - 36th european conference on IR research, ECIR 2014, amsterdam, the netherlands, april 13-16, 2014. proceedings, pp 771–774

  3. Castillo LA, Armengol E, Onaindia E, Sebastia L, Gonzȧlez-boticario J, Rodríguez A, Fernȧndez S, Arias JD, Borrajo D (2008) Samap: An user-oriented adaptive system for planning tourist visits. Expert Syst Appl 34 (2):1318–1332

    Article  Google Scholar 

  4. Chen G, Wu S, Zhou J, Tung AKH (2014) Automatic itinerary planning for traveling services. IEEE Trans Knowl Data Eng 26(3):514–527

    Article  Google Scholar 

  5. Chen X, Zhang Y, Ma P, Li C, Xing C (2015) A package generation and recommendation framework based on travelogues. In: 39Th IEEE annual computer software and applications conference, COMPSAC 2015, taichung, taiwan, july 1-5, 2015. vol 2, pp 692–701

  6. Chen Y, Cheng A, Hsu WH (2013) Travel recommendation by mining people attributes and travel group types from community-contributed photos. IEEE Trans Multimed 15(6):1283–1295

    Article  Google Scholar 

  7. Choudhury MD, Feldman M, Amer-yahia S, Golbandi N, Lempel R, Yu C (2010) Automatic construction of travel itineraries using social breadcrumbs. In: HT’10, Proceedings of the 21st ACM Conference on Hypertext and Hypermedia, Toronto, Ontario, Canada, June 13-16, 2010, pp 35–44

  8. Clements M, Serdyukov P, de Vries AP, Reinders MJT (2011) Personalised travel recommendation based on location co-occurrence. arXiv:1106.5213

  9. Gao Y, Tang J, Hong R, Dai Q, Chua T, Jain R (2010) W2go: a travel guidance system by automatic landmark ranking. In: Proceedings of the 18th international conference on multimedia 2010, Firenze, Italy, October 25-29, 2010, pp 123–132

  10. Graves A (2013) Generating sequences with recurrent neural networks. arXiv:1308.0850

  11. Hao Q, Cai R, Wang C, Xiao R, Yang J, Pang Y, Zhang L (2010) Equip tourists with knowledge mined from travelogues. In: Proceedings of the 19th International Conference on World Wide Web, WWW 2010, Raleigh, North Carolina, USA, April 26-30, 2010, pp 401–410

  12. Hsieh H, Li C, Lin S (2012) Exploiting large-scale check-in data to recommend time-sensitive routes. In: Proceedings of the ACM SIGKDD International Workshop on Urban Computing, UrbComp@KDD 2012, Beijing, China, August 12, 2012, pp 55–62

  13. Hsueh Y, Chen H (2018) Map matching for low-sampling-rate GPS trajectories by exploring real-time moving directions. Inf Sci 433-434:55–69

    Article  MathSciNet  Google Scholar 

  14. Hu G, Shao J, Liu F, Wang Y, Shen HT (2017) If-matching: Towards accurate map-matching with information fusion. IEEE Trans Knowl Data Eng 29(1):114–127

    Article  Google Scholar 

  15. Hu G, Shao J, Ni Z, Zhang D (2018) A graph based method for constructing popular routes with check-ins. World Wide Web. https://doi.org/10.1007/s11280-017-0511-8

  16. Hu G, Shao J, Shen F, Huang Z, Shen HT (2017) Unifying multi-source social media data for personalized travel route planning. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval, Shinjuku, Tokyo, Japan, August 7-11, 2017, pp 893–896

  17. Jiang S, Qian X, Mei T, Fu Y (2016) Personalized travel sequence recommendation on multi-source big social media. IEEE Trans Big Data 2(1):43–56

    Article  Google Scholar 

  18. Kesorn K, Juraphanthong W, Salaiwarakul A (2017) Personalized attraction recommendation system for tourists through check-in data. IEEE Access 5 (26):703–26,721

    Google Scholar 

  19. Kurashima T, Tezuka T, Tanaka K (2006) Mining and visualizing local experiences from blog entries. In: Database and expert systems applications, 17th international conference, DEXA 2006, krakȯw, poland, september 4-8, 2006, proceedings, pp 213–222

  20. Liu X, Zhang R, Meng Z, Hong R, Liu G (2018) On fusing the latent deep cnn feature for image classification. World Wide Web. https://doi.org/10.1007/s11280-018-0600-3

  21. Luo C, Ni B, Yan S, Wang M (2016) Image classification by selective regularized subspace learning. IEEE Trans Multimed 18(1):40–50

    Article  Google Scholar 

  22. Shang S, Chen L, Wei Z, Guo D, Wen J (2016) Dynamic shortest path monitoring in spatial networks. J Comput Sci Technol 31(4):637–648

    Article  Google Scholar 

  23. Shang S, Ding R, Yuan B, Xie K, Zheng K, Kalnis P (2012) User oriented trajectory search for trip recommendation. In: 15Th international conference on extending database technology, EDBT ’12, berlin, germany, march 27-30, 2012, proceedings, pp 156–167

  24. Shang S, Ding R, Zheng K, Jensen CS, Kalnis P, Zhou X (2014) Personalized trajectory matching in spatial networks. VLDB J 23(3):449–468

    Article  Google Scholar 

  25. Shang S, Guo D, Liu J, Wen J (2016) Prediction-based unobstructed route planning. Neurocomputing 213:147–154

    Article  Google Scholar 

  26. Shang S, Guo D, Liu J, Zheng K, Wen J (2016) Finding regions of interest using location based social media. Neurocomputing 173:118–123

    Article  Google Scholar 

  27. Shang S, Liu J, Zheng K, Lu H, Pedersen TB, Wen J (2015) Planning unobstructed paths in traffic-aware spatial networks. GeoInformatica 19(4):723–746

    Article  Google Scholar 

  28. Shang S, Zhu S, Guo D, Lu M (2017) Discovery of probabilistic nearest neighbors in traffic-aware spatial networks. World Wide Web 20(5):1135–1151

    Article  Google Scholar 

  29. Song X, Kanasugi H, Shibasaki R (2016) Deeptransport: Prediction and simulation of human mobility and transportation mode at a citywide level. In: Proceedings of the 50th international joint conference on artificial intelligence, IJCAI 2016, New York, NY, USA, 9-15 July 2016, pp 2618–2624

  30. Taylor K, Lim KH, Chan J (2018) Travel itinerary recommendations with must-see points-of-interest. In: Companion of the the web conference 2018 on the web conference 2018, WWW 2018, lyon, france, april 23-27, 2018, pp 1198–1205

  31. Vansteenwegen P, Oudheusden DV (2007) The mobile tourist guide: an OR opportunity. OR Insight 20(3):21–27

    Article  Google Scholar 

  32. Wang M, Fu W, Hao S, Liu H, Wu X (2017) Learning on big graph: Label inference and regularization with anchor hierarchy. IEEE Trans Knowl Data Eng 29(5):1101–1114

    Article  Google Scholar 

  33. Wang M, Hong R, Li G, Zha Z, Yan S, Chua T (2012) Event driven web video summarization by tag localization and key-shot identification. IEEE Trans Multimed 14(4):975–985

    Article  Google Scholar 

  34. Wang M, Li H, Tao D, Lu K, Wu X (2012) Multimodal graph-based reranking for web image search. IEEE Trans Image Process 21(11):4649–4661

    Article  MathSciNet  Google Scholar 

  35. Wu F, Fu K, Wang Y, Xiao Z, Fu X (2017) A spatial-temporal-semantic neural network algorithm for location prediction on moving objects. Algorithms 10(2):37

    Article  MathSciNet  Google Scholar 

  36. Yin H, Wang C, Yu N, Zhang L (2012) Trip mining and recommendation from geo-tagged photos. In: 2012 IEEE international conference on multimedia and expo workshops, melbourne, australia, july 9-13, 2012, pp 540–545

  37. Yu Z, Xu H, Yang Z, Guo B (2016) Personalized travel package with multi-point-of-interest recommendation based on crowdsourced user footprints. IEEE Trans Human-Machine Syst 46(1):151–158

    Article  Google Scholar 

  38. Zheng B, Wang H, Zheng K, Su H, Liu K, Shang S (2018) Sharkdb: an in-memory column-oriented storage for trajectory analysis. World Wide Web 21 (2):455–485

    Article  Google Scholar 

  39. Zheng VW, Zheng Y, Xie X, Yang Q (2010) Collaborative location and activity recommendations with GPS history data. In: Proceedings of the 19th international conference on World Wide Web, WWW 2010, Raleigh, North Carolina, USA, April 26-30, 2010, pp 1029–1038

  40. Zheng Y, Zhang L, Ma Z, Xie X, Ma W (2011) Recommending friends and locations based on individual location history. TWEB 5(1):5:1–5:44

    Article  Google Scholar 

  41. Zheng Y, Zhang L, Xie X, Ma W (2009) Mining interesting locations and travel sequences from GPS trajectories. In: Proceedings of the 18th international conference on World Wide Web, WWW 2009, Madrid, Spain, April 20-24, 2009, pp 791–800

  42. Zhu J, Jiang W, Liu A, Liu G, Zhao L (2017) Effective and efficient trajectory outlier detection based on time-dependent popular route. World Wide Web 20(1):111–134

    Article  Google Scholar 

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (grants No. 61672133 and No. 61632007).

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Authors and Affiliations

  1. Center for Future Media & School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

    Gang Hu, Yi Qin & Jie Shao

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  1. Gang Hu
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  2. Yi Qin
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  3. Jie Shao
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Correspondence to Jie Shao.

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Hu, G., Qin, Y. & Shao, J. Personalized travel route recommendation from multi-source social media data. Multimed Tools Appl 79, 33365–33380 (2020). https://doi.org/10.1007/s11042-018-6776-9

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