Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/29887
Title: Personality-Informed Restaurant Recommendation
Authors: Christodoulou, Evripides 
Gregoriades, Andreas 
Pampaka, Maria 
Herodotou, Herodotos 
Major Field of Science: Engineering and Technology
Field Category: Electrical Engineering - Electronic Engineering - Information Engineering
Keywords: Personality;Recommendation systems;Tourism;XGBoost
Issue Date: 12-Apr-2022
Source: 10th World Conference on Information Systems and Technologies, WorldCIST 2022, Budva, 12 - 14 April 2022
Volume: 468 LNNS
Start page: 13
End page: 21
Journal: Lecture Notes in Networks and Systems 
Abstract: Recommendation systems are popular tools assisting consumers with the over-choice problem; however, they have been criticized of insufficient performance in highly complex domains. This work focuses on the analysis of consumers’ personalities, due to its recent popularity in recommender systems, within topics discussed by users in electronic word of mouth (e-WOM) to improve the recommendation of restaurants to tourists. The proposed method utilizes structured and unstructured data from online reviews to predict the probability of a user enjoying a restaurant he/she had not visited before and based on that make recommendations to different users. A personality classification model that analyses the textual information of reviews and predicts the personality of the author is employed. Topic modelling is used to identify additional features that characterize users’ preferences and restaurants features. Structured information of reviews such as restaurants’ price-range, cuisine type, and value for money are extracted and used in the prediction process. The aforementioned features are used to train an extreme gradient boosting tree model which outputs the user rating of restaurants. The trained model is compared against popular recommendation techniques such as nonnegative matrix factorization and single value decomposition.
Description: Lecture Notes in Networks and Systems book series, vol. 468 LNNS, pp. 13 - 21
URI: https://hdl.handle.net/20.500.14279/29887
ISBN: 9783031048258
ISSN: 23673370
DOI: 10.1007/978-3-031-04826-5_2
Rights: © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
Attribution-NonCommercial-NoDerivatives 4.0 International
Type: Conference Papers
Affiliation : Cyprus University of Technology 
University of Manchester 
Appears in Collections:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation

CORE Recommender
Show full item record

SCOPUSTM   
Citations

3
checked on Mar 14, 2024

Page view(s)

117
Last Week
1
Last month
2
checked on Jun 23, 2024

Google ScholarTM

Check

Altmetric


This item is licensed under a Creative Commons License Creative Commons