Abstract
In this paper, we propose a system for recommending tours using online customer reviews. The system receives a review about tours written by a user, estimates the user’s interests about tours from the review, and provides reviews written by others to the user based on interest matching. In the recommendation system, the user’s interests about tours described in a review are modelled as a preference vector. The preference vector of a review is defined by improving the word2vec model. Evaluating the similarity between preference vectors of the user’s review and each review in a database, the system finds reviews which have similar interests to the user and recommends the tours mentioned in the found reviews. From qualitative evaluation using questionnaires, we confirmed that the proposed system provided more useful information to the user than conventional recommendation systems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Almeida A, Martins C (2016) Gathering data for professional tourism points of interest. In: International conference on computer science and software engineering, pp 125–126
Bai X, Chen F, Zhan S (2014) A new clustering model based on word2vec mining on sina weibo users’ tags. Int J Grid Distrib Comput 7
Brown B (2012) Beyond recommendations:local review web sites and their impact. ACM Trans Comput Hum Interact (TOCHI) 19(4):1–24
Hayashi T, Seino Y (2017) A method for discriminating travel reviews from commercial speech. J Global Tour Res 2(1):33–38
Huang C, Tsai C, Chen W (2012) A personalized tour recommendation system over cloud. In: Human centric technology and service in smart space. Springer, pp 297–302
Jones KS (1972) A statistical interpretation of term specificity and its application in retrieval. J Doc 60(1):493–502
Kawai Y, Zhang J, Kawasaki H (2009) Tour recommendation system based on web information and GIS. In: IEEE Int Conf Multimed Expo, pp 990–993
Kenter T, Rijke MD (2015) Short text similarity with word embeddings, pp 1411–1420
Kusner MJ, Sun Y et al. (2015) From word embeddings to document distances. In: International conference on international conference on machine learning, pp 957–966
Le Q, Mikolov T (2014) Distributed representations of sentences and documents 4:1188–1196
Liu B, Zhang L (2012) A survey of opinion mining and sentiment analysis. Springer, US
Mikolov T, Chen K et al (2013) Efficient estimation of word representations in vector space. Comput Sci 1301:3781
Pang B, Lee L (2008) Opinion mining and sentiment analysis. Found Trends Inf Retr 2:1–135
Robertson SE, Jones KS (1988) Relevance weighting of search terms. Taylor Graham Publishing
Santos F, Almeida A et al (2016) Tourism recommendation system based in user’s profile and functionality levels. In: Proceedings of the 9th international conference on computer science and software engineering, pp 93–97
Ye Q, Law R et al (2011) The influence of user-generated content on traveler behavior: An empirical investigation on the effects of e-word-of-mouth to hotel online bookings. Comput Hum Behav 27(2):634–639
Zehrer A, Crotts JC, Magnini VP (2011) The perceived usefulness of blog postings: An extension of the expectancy-disconfirmation paradigm. Tour Manag 32(1):106–113
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Hayashi, T., Yoshida, T. (2019). Development of a Tour Recommendation System Using Online Customer Reviews. In: Xu, J., Cooke, F., Gen, M., Ahmed, S. (eds) Proceedings of the Twelfth International Conference on Management Science and Engineering Management. ICMSEM 2018. Lecture Notes on Multidisciplinary Industrial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-93351-1_90
Download citation
DOI: https://doi.org/10.1007/978-3-319-93351-1_90
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93350-4
Online ISBN: 978-3-319-93351-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)