A Context-Aware Location...(sustainability MDPI, 2020.05) > 자유게시판

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논문명

A Context-Aware Location Recommendation System for Tourists Using Hierarchical LSTM Model

Abstract

​The significance of contextual data has been recognized by analysts and specialists in numerous disciplines such as customization, data recovery, ubiquitous and versatile processing, information mining, and management. While a generous research has just been performed in the zone of recommender frameworks, by far most of the existing approaches center on prescribing the most relevant items to customers. It usually neglects extra-contextual information, for example time, area, climate or the popularity of different locations. Therefore, we proposed a deep long-short term 

memory (LSTM) based context-enriched hierarchical model. This proposed model had two levels of hierarchy and each level comprised of a deep LSTM network. In each level, the task of the LSTM was different. At the first level, LSTM learned from user travel history and predicted the next location probabilities. A contextual learning unit was active between these two levels. This unit extracted maximum possible contexts related to a location, the user and its environment such as weather, climate and risks. This unit also estimated other effective parameters such as the popularity of a location. To avoid feature congestion, XGBoost was used to rank feature importance. The features

with no importance were discarded. At the second level, another LSTM framework was used to learn these contextual features embedded with location probabilities and resulted into top ranked places. The performance of the proposed approach was elevated with an accuracy of 97.2%, followed

by gated recurrent unit (GRU) (96.4%) and then Bidirectional LSTM (94.2%). We also performed experiments to find the optimal size of travel history for effective recommendations.

논문 정보 

​Wafa Shafqat and Yung-Cheol Byun * 

Department of Computer Engineering, Jeju National University, Jeju 63243, Korea; wafashafqat92@gmail.com

* Correspondence: ycb@jejunu.ac.kr

Sustainability 2020, 12, 4107; doi:10.3390/su12104107 

www.mdpi.com/journal/sustainability