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RecPOID: POI Recommendation with Friendship Aware and Deep CNN

Safavi, Sadaf; Jalali, Mehrdad ORCID iD icon

Abstract (englisch):
In location-based social networks (LBSNs), exploit several key features of points-of-interest (POIs) and users on precise POI recommendation be significant. In this work, a novel POI recommenda-tion pipeline based on the convolutional neural network named RecPOID is proposed, which can recommend an accurate sequence of top-k POIs and considers only the effect of the most similar pattern friendship rather than all user’s friendship. We use the fuzzy c-mean clustering method to find the similarity. Temporal and spatial features of similar friends are fed to our Deep CNN model. The 10-layer convolutional neural network can predict longitude and latitude and the Id of the next proper locations; after that, based on the shortest time distance from a similar pattern’s friendship, select the smallest distance locations. The proposed structure uses six features, includ-ing user’s ID, month, day, hour, minute, and second of visiting time by each user as inputs. RecPOID based on two accessible LBSNs datasets is evaluated. Experimental outcomes illustrate considering most similar friendship could improve the accuracy of recommendations and the proposed RecPOID for POI recommendation outperforms state-of-the-art approaches.

Verlagsausgabe §
DOI: 10.5445/IR/1000130826
Veröffentlicht am 23.03.2021
DOI: 10.3390/fi13030079
Zitationen: 2
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Funktionelle Grenzflächen (IFG)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 1999-5903
KITopen-ID: 1000130826
HGF-Programm 43.33.11 (POF IV, LK 01) Adaptive and Bioinstructive Materials Systems
Erschienen in Future Internet
Verlag MDPI
Band 13
Heft 3
Seiten Article no: 79
Vorab online veröffentlicht am 22.03.2021
Nachgewiesen in Dimensions
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