ELR-DC

An Efficient Recommendation Scheme for Location Based Social Networks

Ruheng Lv, Yufeng Wang, Qun Jin, Jianhua Ma

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Citation (Scopus)

    Abstract

    Location-based social networks (LBSNs) have recently attracted millions of mobile users to share their locations and location-related contents. With the increasing use of LBSNs, an efficient personalized recommendation service is required to recommend appropriate point of interests (POIs) to users. Traditional collaborative filtering (CF) based recommendation algorithms need go through all users in LBSN to recommend locations to the target user. Due to the fact that many users are irrelevant to the target user, these approaches perform poorly in accuracy and scalability. In this paper, we propose an Efficient Location Recommendation scheme based on Discrete particle swarm optimization (DPSO) and Collaborative filtering, called ELR-DC. This scheme efficiently detects communities with close internal ties and then conducts location recommendation in each community. Specifically, a similarity network among users is firstly constructed based on their check-in activities, which explicitly takes into account users' similarities of interest and active regions. Then, an improved merging DPSO algorithm (IMDPSO) is proposed to detect communities through utilizing the formed similarity network. Then, in each community, CF algorithm is applied to recommend Top-N locations to each user. Finally, we conduct a comprehensive performance evaluation on a large-scale datasets collected from Gowalla. Experimental results show that the proposed scheme have the superiority of the precision and efficiency over the existed CF algorithms.

    Original languageEnglish
    Title of host publicationProceedings - 2016 IEEE International Conference on Internet of Things; IEEE Green Computing and Communications; IEEE Cyber, Physical, and Social Computing; IEEE Smart Data, iThings-GreenCom-CPSCom-Smart Data 2016
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages567-572
    Number of pages6
    ISBN (Electronic)9781509058808
    DOIs
    Publication statusPublished - 2017 May 1
    Event9th IEEE International Conference on Internet of Things, 12th IEEE International Conference on Green Computing and Communications, 9th IEEE International Conference on Cyber, Physical, and Social Computing and 2016 IEEE International Conference on Smart Data, iThings-GreenCom-CPSCom-Smart Data 2016 - Chengdu, China
    Duration: 2016 Dec 162016 Dec 19

    Other

    Other9th IEEE International Conference on Internet of Things, 12th IEEE International Conference on Green Computing and Communications, 9th IEEE International Conference on Cyber, Physical, and Social Computing and 2016 IEEE International Conference on Smart Data, iThings-GreenCom-CPSCom-Smart Data 2016
    CountryChina
    CityChengdu
    Period16/12/1616/12/19

    Fingerprint

    social network
    Collaborative filtering
    Particle swarm optimization (PSO)
    community
    Merging
    Scalability
    efficiency
    evaluation
    performance

    Keywords

    • Collaborative Filtering (CF)
    • Community detection
    • Discrete Particle Swarm Optimization (DPSO)
    • Location recommendation

    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Safety, Risk, Reliability and Quality
    • Communication

    Cite this

    Lv, R., Wang, Y., Jin, Q., & Ma, J. (2017). ELR-DC: An Efficient Recommendation Scheme for Location Based Social Networks. In Proceedings - 2016 IEEE International Conference on Internet of Things; IEEE Green Computing and Communications; IEEE Cyber, Physical, and Social Computing; IEEE Smart Data, iThings-GreenCom-CPSCom-Smart Data 2016 (pp. 567-572). [7917155] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData.2016.127

    ELR-DC : An Efficient Recommendation Scheme for Location Based Social Networks. / Lv, Ruheng; Wang, Yufeng; Jin, Qun; Ma, Jianhua.

    Proceedings - 2016 IEEE International Conference on Internet of Things; IEEE Green Computing and Communications; IEEE Cyber, Physical, and Social Computing; IEEE Smart Data, iThings-GreenCom-CPSCom-Smart Data 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 567-572 7917155.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Lv, R, Wang, Y, Jin, Q & Ma, J 2017, ELR-DC: An Efficient Recommendation Scheme for Location Based Social Networks. in Proceedings - 2016 IEEE International Conference on Internet of Things; IEEE Green Computing and Communications; IEEE Cyber, Physical, and Social Computing; IEEE Smart Data, iThings-GreenCom-CPSCom-Smart Data 2016., 7917155, Institute of Electrical and Electronics Engineers Inc., pp. 567-572, 9th IEEE International Conference on Internet of Things, 12th IEEE International Conference on Green Computing and Communications, 9th IEEE International Conference on Cyber, Physical, and Social Computing and 2016 IEEE International Conference on Smart Data, iThings-GreenCom-CPSCom-Smart Data 2016, Chengdu, China, 16/12/16. https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData.2016.127
    Lv R, Wang Y, Jin Q, Ma J. ELR-DC: An Efficient Recommendation Scheme for Location Based Social Networks. In Proceedings - 2016 IEEE International Conference on Internet of Things; IEEE Green Computing and Communications; IEEE Cyber, Physical, and Social Computing; IEEE Smart Data, iThings-GreenCom-CPSCom-Smart Data 2016. Institute of Electrical and Electronics Engineers Inc. 2017. p. 567-572. 7917155 https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData.2016.127
    Lv, Ruheng ; Wang, Yufeng ; Jin, Qun ; Ma, Jianhua. / ELR-DC : An Efficient Recommendation Scheme for Location Based Social Networks. Proceedings - 2016 IEEE International Conference on Internet of Things; IEEE Green Computing and Communications; IEEE Cyber, Physical, and Social Computing; IEEE Smart Data, iThings-GreenCom-CPSCom-Smart Data 2016. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 567-572
    @inproceedings{49925b4720f7496e8fcd3f896ea14b6b,
    title = "ELR-DC: An Efficient Recommendation Scheme for Location Based Social Networks",
    abstract = "Location-based social networks (LBSNs) have recently attracted millions of mobile users to share their locations and location-related contents. With the increasing use of LBSNs, an efficient personalized recommendation service is required to recommend appropriate point of interests (POIs) to users. Traditional collaborative filtering (CF) based recommendation algorithms need go through all users in LBSN to recommend locations to the target user. Due to the fact that many users are irrelevant to the target user, these approaches perform poorly in accuracy and scalability. In this paper, we propose an Efficient Location Recommendation scheme based on Discrete particle swarm optimization (DPSO) and Collaborative filtering, called ELR-DC. This scheme efficiently detects communities with close internal ties and then conducts location recommendation in each community. Specifically, a similarity network among users is firstly constructed based on their check-in activities, which explicitly takes into account users' similarities of interest and active regions. Then, an improved merging DPSO algorithm (IMDPSO) is proposed to detect communities through utilizing the formed similarity network. Then, in each community, CF algorithm is applied to recommend Top-N locations to each user. Finally, we conduct a comprehensive performance evaluation on a large-scale datasets collected from Gowalla. Experimental results show that the proposed scheme have the superiority of the precision and efficiency over the existed CF algorithms.",
    keywords = "Collaborative Filtering (CF), Community detection, Discrete Particle Swarm Optimization (DPSO), Location recommendation",
    author = "Ruheng Lv and Yufeng Wang and Qun Jin and Jianhua Ma",
    year = "2017",
    month = "5",
    day = "1",
    doi = "10.1109/iThings-GreenCom-CPSCom-SmartData.2016.127",
    language = "English",
    pages = "567--572",
    booktitle = "Proceedings - 2016 IEEE International Conference on Internet of Things; IEEE Green Computing and Communications; IEEE Cyber, Physical, and Social Computing; IEEE Smart Data, iThings-GreenCom-CPSCom-Smart Data 2016",
    publisher = "Institute of Electrical and Electronics Engineers Inc.",
    address = "United States",

    }

    TY - GEN

    T1 - ELR-DC

    T2 - An Efficient Recommendation Scheme for Location Based Social Networks

    AU - Lv, Ruheng

    AU - Wang, Yufeng

    AU - Jin, Qun

    AU - Ma, Jianhua

    PY - 2017/5/1

    Y1 - 2017/5/1

    N2 - Location-based social networks (LBSNs) have recently attracted millions of mobile users to share their locations and location-related contents. With the increasing use of LBSNs, an efficient personalized recommendation service is required to recommend appropriate point of interests (POIs) to users. Traditional collaborative filtering (CF) based recommendation algorithms need go through all users in LBSN to recommend locations to the target user. Due to the fact that many users are irrelevant to the target user, these approaches perform poorly in accuracy and scalability. In this paper, we propose an Efficient Location Recommendation scheme based on Discrete particle swarm optimization (DPSO) and Collaborative filtering, called ELR-DC. This scheme efficiently detects communities with close internal ties and then conducts location recommendation in each community. Specifically, a similarity network among users is firstly constructed based on their check-in activities, which explicitly takes into account users' similarities of interest and active regions. Then, an improved merging DPSO algorithm (IMDPSO) is proposed to detect communities through utilizing the formed similarity network. Then, in each community, CF algorithm is applied to recommend Top-N locations to each user. Finally, we conduct a comprehensive performance evaluation on a large-scale datasets collected from Gowalla. Experimental results show that the proposed scheme have the superiority of the precision and efficiency over the existed CF algorithms.

    AB - Location-based social networks (LBSNs) have recently attracted millions of mobile users to share their locations and location-related contents. With the increasing use of LBSNs, an efficient personalized recommendation service is required to recommend appropriate point of interests (POIs) to users. Traditional collaborative filtering (CF) based recommendation algorithms need go through all users in LBSN to recommend locations to the target user. Due to the fact that many users are irrelevant to the target user, these approaches perform poorly in accuracy and scalability. In this paper, we propose an Efficient Location Recommendation scheme based on Discrete particle swarm optimization (DPSO) and Collaborative filtering, called ELR-DC. This scheme efficiently detects communities with close internal ties and then conducts location recommendation in each community. Specifically, a similarity network among users is firstly constructed based on their check-in activities, which explicitly takes into account users' similarities of interest and active regions. Then, an improved merging DPSO algorithm (IMDPSO) is proposed to detect communities through utilizing the formed similarity network. Then, in each community, CF algorithm is applied to recommend Top-N locations to each user. Finally, we conduct a comprehensive performance evaluation on a large-scale datasets collected from Gowalla. Experimental results show that the proposed scheme have the superiority of the precision and efficiency over the existed CF algorithms.

    KW - Collaborative Filtering (CF)

    KW - Community detection

    KW - Discrete Particle Swarm Optimization (DPSO)

    KW - Location recommendation

    UR - http://www.scopus.com/inward/record.url?scp=85020223781&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=85020223781&partnerID=8YFLogxK

    U2 - 10.1109/iThings-GreenCom-CPSCom-SmartData.2016.127

    DO - 10.1109/iThings-GreenCom-CPSCom-SmartData.2016.127

    M3 - Conference contribution

    SP - 567

    EP - 572

    BT - Proceedings - 2016 IEEE International Conference on Internet of Things; IEEE Green Computing and Communications; IEEE Cyber, Physical, and Social Computing; IEEE Smart Data, iThings-GreenCom-CPSCom-Smart Data 2016

    PB - Institute of Electrical and Electronics Engineers Inc.

    ER -