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" A Grouping Hotel Recommender System Based on Deep Learning and Sentiment Analysis "


Record Number : 1502321
Language of Document : English
Main Entry : Fatemeh Abbasi
Title & Author : A Grouping Hotel Recommender System Based on Deep Learning and Sentiment Analysis [electronic resources: essay]/ Fatemeh Abbasi؛ Ameneh Khadivar؛ Mohsen Yazdinejad
Piece Level : Journal of Information Technology Management
Notes Pertaining to Publication, Distribution, Etc. : Research Paper
: September 2019
Access Link : https://jitm.ut.ac.ir/article_73948.html
: https://jitm.ut.ac.ir/article_73948_eac728ff5cdfbea6a1a002dcfcd48575.pdf
Summary or Abstract : Recommender systems are important tools for users to identify their preferred items and for businesses to improve their products and services. In recent years, the use of online services for selection and reservation of hotels have witnessed a booming growth. Customer’ reviews have replaced the word of mouth marketing, but searching hotels based on user priorities is more time-consuming. This study is aimed at designing a recommender system based on the explicit and implicit preferences of the customers in order to increase prediction’s accuracy. In this study, we have combined sentiment analysis with the Collaborative Filtering (CF) based on deep learning for user groups in order to increase system accuracy. The proposed system uses Natural Language Processing (NLP) and supervised classification approach to analyze sentiments and extract implicit features. In order to design the recommender system, the Singular Value Decomposition (SVD) was used to improve scalability. The results show that our proposed method improves CF performance.
Topical Name Used as Subject : Deep learning
: Sentiment analysis
: Singular Value Decomposition (SVD)
: Grouping recommender systems
Personal Name - Alternative Intelectual Responsibility : Ameneh Khadivar
: Mohsen Yazdinejad
Originating Source : University of Tehran. Central Library and Documentation Center
copyhttps://lib.ut.ac.ir/site/catalogue/1502321
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