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Title:
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PERSONALIZED SIGHTSEEING SPOT RECOMMENDATION BASED ON READILY ACCESSIBLE DATA
- APPLYING SEMI-SUPERVISED LEARNING WITH A BAYESIAN CLASSIFICATION METHOD-
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Author(s):
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Satona Shirai, Hiroshi Uehara and Kazuya Suzuki |
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ISBN:
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978-989-8704-36-8 |
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Editors:
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Piet Kommers, Tomayess Issa, Adriana Backx Noronha Viana, Theodora Issa and Pedro Isaías |
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Year:
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2021 |
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Edition:
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Single |
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Keywords:
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Data Mining, Data Analytics, Recommendation, Tourism, Machine Learning |
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Type:
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Full |
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First Page:
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77 |
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Last Page:
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83 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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This study proposes to provide personalized sightseeing spot recommendation for foreign tourists on the basis of their
preferences acquired from an augmented dataset. This dataset has a wide variety of sightseeing spots including
lesser-known spots for foreign tourists that does not compromise the preferences of foreigners. Although
recommendation systems for tourism have been widely studied, many of these studies have attempted to develop
personalized recommendation by acquiring personal preferences from their touring activity histories, which are not
readily accessible, so they have been restricted to limited empirical data for their evaluations. In this study, we attempt to
extract personal preferences from the readily accessible data of Tripadvisor and represent each preference as a probability
distribution to derive possible sightseeing spots to be recommended. The proposal was applied to reviews written by
foreign tourists visiting sightseeing spots in Japan, and resulted in suitable sightseeing spots being recommended
including serendipitous ones suitable to their personal preferences. |
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