A Grouping Hotel Recommender System Based on Deep Learning and Sentiment Analysis | ||
| Journal of Information Technology Management | ||
| مقاله 3، دوره 11، شماره 2، 2019، صفحه 59-78 اصل مقاله (1.28 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/jitm.2019.289271.2402 | ||
| نویسندگان | ||
| Fatemeh Abbasi1؛ Ameneh Khadivar* 2؛ Mohsen Yazdinejad3 | ||
| 1Postdoctoral researcher, Department of Social and Economic, Alzahra University, Tehran, Iran. | ||
| 2Associate Professor, Department of Social and Economic, Alzahra University, Tehran, Iran. | ||
| 3Ph.D. Student, Faculty of Computing Engineering, University of Isfahan , Iran. | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Grouping recommender systems؛ Sentiment analysis؛ Deep learning؛ Singular Value Decomposition (SVD) | ||
| مراجع | ||
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