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RecPOID: POI Recommendation with Friendship Aware and Deep CNN

Author

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  • Sadaf Safavi

    (Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad 9187147578, Iran)

  • Mehrdad Jalali

    (Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad 9187147578, Iran
    Institute of Functional Interfaces, Karlsruhe Institute of Technology (KIT), 76344 Karlsruhe, Germany)

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.

Suggested Citation

  • Sadaf Safavi & Mehrdad Jalali, 2021. "RecPOID: POI Recommendation with Friendship Aware and Deep CNN," Future Internet, MDPI, vol. 13(3), pages 1-14, March.
    • Handle: RePEc:gam:jftint:v:13:y:2021:i:3:p:79-:d:521871
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    References listed on IDEAS

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    1. Kim, Tae-Young & Cho, Sung-Bae, 2019. "Predicting residential energy consumption using CNN-LSTM neural networks," Energy, Elsevier, vol. 182(C), pages 72-81.
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