RecPOID: POI Recommendation with Friendship Aware and Deep CNN

Sadaf Safavi,Mehrdad Jalali

doi:10.3390/fi13030079

Abstract

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 recommendation 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, including 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.

Highlights

In Yelp, ours obtains 0.037 and 0.032 in terms of Precision@5 and Precision@10, respectively. It means our RecPOID method exhibiting 0.01, 0.015, and 0.02 performance improvement compared with UFC, LFBCA, and LORE, respectively
A novel framework for POI recommendation named RecPOID has been proposed by incorporating check-in correlation, the significance of the friend, and user preference
A fuzzy clustering approach has been utilized to find the more important friendship to apply a powerful relationship in exploring user preference

Summary

Introduction

In location-based social networks (LBSNs), exploit several key features of points-of-interest (POIs) and users on precise POI recommendation be significant. A novel POI recommendation 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. The 10-layer convolutional neural network can predict longitude and latitude and the Id of the 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, including user’s ID, month, day, hour, minute, and second of visiting time by each user as inputs. By 2020, Foursquare has over 55 million users per month and over three billion monthly visits to various locations worldwide; the Swarm app has nine million check-ins per day

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Conclusion