Abstract
For tourist attraction recommendation, there are three essential aspects to be considered: tourist preferences, attraction themes, and sentiments on themes of attraction. By utilizing vast multi-modal media available on Internet, this paper is aiming to develop an efficient solution of tourist attraction recommendation covering all these three aspects. To achieve this goal, we propose a probabilistic generative model called Sentiment-aware Multi-modal Topic Model (SMTM), whose advantages are four folds: (1) we separate tourists and attractions into two domains for better recovering tourist topics and attraction themes; (2) we investigate tourists sentiments on topics to retain the preference ones; (3) the recommended attraction is guaranteed with positive sentiment on the related attraction themes; (4) the multi-modal data are utilized to enhance the recommendation accuracy. Qualitative and quantitative evaluation results have validated the effectiveness of our method.
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Notes
- 1.
[online]. Available https://www.tripadvisor.in/.
- 2.
[online]. Available http://nlp.stanford.edu/software/index.shtml.
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Acknowledgement
This work is supported by the National Key Research & Development Plan of China (No. 2017YFB1002800), by the National Natural Science Foundation of China under Grant 61872424, 61572503, 61720106006, 61432019, and by NUPTSF (No. NY218001), also supported by the Key Research Program of Frontier Sciences, CAS, Grant NO. QYZDJ-SSW-JSC039, and the K.C.Wong Education Foundation.
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Wang, J., Bao, BK., Xu, C. (2019). Sentiment-Aware Multi-modal Recommendation on Tourist Attractions. In: Kompatsiaris, I., Huet, B., Mezaris, V., Gurrin, C., Cheng, WH., Vrochidis, S. (eds) MultiMedia Modeling. MMM 2019. Lecture Notes in Computer Science(), vol 11295. Springer, Cham. https://doi.org/10.1007/978-3-030-05710-7_1
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