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(PDF) A design for a tourist CF system
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In this paper, design method of double rubber mount system, it's effect of vibration isolation and the measurement result of underwater noise level radiated by ...
Design of a Bayesian Recommender System for Tourists ...
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This thesis presents a systematic literature review of the recommender system literature identifying nine types of solutions to the cold-start user problem, ...
Call for Contributions (Extended abstracts) 74th ...
Aiest
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(1) Designing tourism systems for a sustainable future. (2) Human & machine relations in tourism. (3) AIEST's Advances in Tourism Research -. Perspectives of ...
book of extended abstracts
projeto SHIFT
https://shiftresearch.pt › 2024/04 › antecipating
projeto SHIFT
https://shiftresearch.pt › 2024/04 › antecipating
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由 CA Martins 著作2024 — Design/methodology/approach – The paper overviews the key results of a questionnaire survey among 555 entrepreneurs of the Portuguese tourism and hospitality ...
166 頁
Call for Contributions (Extended abstracts) 72nd ...
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2023年2月28日 — (1) Designing tourism systems for a sustainable future. (2) Transiting towards future tourism destinations through smart and green development.
EXTENDED ABSTRACTS
Princeton University
https://www.princeton.edu › ~ndaw › RLDM17E...
Princeton University
https://www.princeton.edu › ~ndaw › RLDM17E...
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Online reinforcement learning (RL) is increasingly popular for the personalized mobile health (mHealth) intervention. It is able to personalize the type and ...
354 頁
Personalized Tourist Recommender System: A Data- ...
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由 D Shrestha 著作2024被引用 4 次 — This study introduces a data-driven and machine-learning approach to design a personalized tourist recommendation system for Nepal.
Author Instructions for Extended Abstract
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由 Y Xie 著作被引用 2 次 — Users may download and print one copy of any publication from the public portal for the purpose of private study or research.
Design and Implementation of an Optimal Travel Route ...
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由 L Hang 著作2018被引用 24 次 — In this paper, we propose an optimal travel route recommender system by analyzing the data history of previous users.