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Sentiment-Aware Multi-modal Recommendation on Tourist Attractions

  • Junyi Wang
  • Bing-Kun BaoEmail author
  • Changsheng Xu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11295)

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.

Keywords

Tourism recommendation Multi-modal computing Topic model Sentiment analysis 

Notes

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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Hefei University of TechnologyHefeiChina
  2. 2.College of Telecommunications and Information EngineeringNanjing University of Posts and TelecommunicationsNanjingChina
  3. 3.National Lab of Pattern RecognitionInstitute of Automation, CASBeijingChina
  4. 4.University of Chinese Academy of SciencesBeijingChina

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