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Erschienen in:

2019 | OriginalPaper | Buchkapitel

Sentiment-Aware Multi-modal Recommendation on Tourist Attractions

verfasst von : Junyi Wang, Bing-Kun Bao, Changsheng Xu

Erschienen in: MultiMedia Modeling

Verlag: Springer International Publishing

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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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Vorheriges Kapitel Correction to: MultiMedia Modeling
Nächstes Kapitel SCOD: Dynamical Spatial Constraints for Object Detection
Fußnoten
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Metadaten
Titel
Sentiment-Aware Multi-modal Recommendation on Tourist Attractions
verfasst von
Junyi Wang
Bing-Kun Bao
Changsheng Xu
Copyright-Jahr
2019
Buch
MultiMedia Modeling

Print ISBN: 978-3-030-05709-1

Electronic ISBN: 978-3-030-05710-7

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-05710-7_1