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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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Acknowledgements

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

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  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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