Back to Search

Developing session-based personalized accommodation recommender system by using LSTM

Title	Developing session-based personalized accommodation recommender system by using LSTM
Author	Can, Y. S., Erkut, H., Giritli, E. B., Kutluay, H., Buyukoguz, K., Demiroğlu, Cenk
Publication Date:	2022
Publication Place	- IEEE
Subject	Hotel recommendation, LSTM, Session-based recommender systems, Tourism
Type	Document
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	978-166545092-8
Record ID	dbd5afb8-8a15-4a33-af69-97f5e0bc859e
Library Location	Electrical & Electronics Engineering
Date	2022
Sample Text	Tourism sector has been transformed by the advances in the Internet technology. Users can search for information and can select their destination from various alternatives by themselves, which brings the need for personal recommender methods. Personalized recommender system development is a complex topic. Demographic information, series of user clicks, and interactions and hotel features are examined to offer the appropriate set of hotels. Since the user interactions, clicks and hotel history is a time series data, Long Short-Term Memory models is a perfect fit to recommend a set of hotels from this data. In this study, we proposed a session-based accommodation recommender system that uses LSTM and achieved promising results.
DOI	10.1109/SIU55565.2022.9864733

View in source Özyeğin University Özyeğin University - Historical works, archives, and periodicals search engine

Özyeğin University

Developing session-based personalized accommodation recommender system by using LSTM

Author Can, Y. S., Erkut, H., Giritli, E. B., Kutluay, H., Buyukoguz, K., Demiroğlu, Cenk

Publication Date 2022

Publication Place - IEEE

Subject Hotel recommendation, LSTM, Session-based recommender systems, Tourism

Type Document

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 978-166545092-8

Record ID dbd5afb8-8a15-4a33-af69-97f5e0bc859e

Library Location Electrical & Electronics Engineering

Date 2022

Sample Text Tourism sector has been transformed by the advances in the Internet technology. Users can search for information and can select their destination from various alternatives by themselves, which brings the need for personal recommender methods. Personalized recommender system development is a complex topic. Demographic information, series of user clicks, and interactions and hotel features are examined to offer the appropriate set of hotels. Since the user interactions, clicks and hotel history is a time series data, Long Short-Term Memory models is a perfect fit to recommend a set of hotels from this data. In this study, we proposed a session-based accommodation recommender system that uses LSTM and achieved promising results.

DOI 10.1109/SIU55565.2022.9864733