An Intelligent Touring Recommender System Using Techniques of User Filtering, Air Quality Prediction and Health Care 

Ja-Hwung Su¹, Yi-Wen Liao², Yu-Wei Zhao², Jin-Ming Chen², Ding-En Syu² 
(1. Department of Computer Science and Information Engineering, National University of Kaohsiung, Taiwan;

2. Department of Information Management, Cheng Shiu University, Kaohsiung, Taiwan)

Abstract: In recent years, the modern traffic technology has enabled a large demand of touring. Indeed, a good touring can ease our tight life. Thus, how to provide a good touring recommendation has been a hot topic over the past few years. For this topic, most recent touring websites make attempts to provide a user-oriented service by user constraints without considering personality, environmental information and traveler health care. To cope with these issues, in this paper, we propose a personalized touring recommender system that provides the user with recommendation of touring lists, information of air quality and service of health care. In terms of personalization, the user-filtering is performed to predict the user touring preference by personality similarities and user ratings. In terms of air quality, the Long-Short-Term-Memory (LSTM) is performed to predict the air qualities of touring locations (called points in this paper). In terms of health care, the user can acquire the locations of pharmacies and hospitals for touring emergency. The experimental results reveal that, through the proposed system, a high-quality of touring recommendation can be achieved. 

Key words: recommender system; touring; user filtering; air quality prediction; health care

JEL codes: Z, Z3, Z32