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A personalized point-of-interest recommendation system for O2O commerce

Author

Listed:
  • Laisong Kang

    (Beijing Jiaotong University)

  • Shifeng Liu

    (Beijing Jiaotong University)

  • Daqing Gong

    (Beijing Jiaotong University)

  • Mincong Tang

    (Beijing Jiaotong University)

Abstract

Online-to-offline (O2O) commerce, e.g., the internet celebrity economy, provides a seamless service experience between online commerce and offline bricks-and-mortar commerce. This type of commerce model is closely related to location-based social networks (LBSNs), which incorporate mobility patterns and human social ties. Personalized point-of-interest (POI) recommendations are crucial for O2O commerce in LBSNs; such recommendations not only help users explore new venues but also enable many location-based services, e.g., the targeting of mobile advertisements to users. However, producing personalized POI recommendations for O2O commerce is highly challenging, since LBSNs involve heterogeneous types of data and the user-POI matrix is very sparse. LBSNs have substantially altered how people interact by sharing a wide range of user information, such as the products and services that users use and the places and events that users visit. To address these challenges in O2O commerce LBSNs, we analyze users’ check-in behaviors in detail and introduce the concept of a heterogeneous information network (HIN). Then, we propose a HIN-based POI recommendation system, which consists of two components: an improved singular value decomposition (SVD++) and factorization machines (FMs). The results of experiments on two real-world O2O commerce websites, namely, Gowalla and Foursquare, demonstrate that our method is more accurate than baseline methods. Additionally, a case study of the bricks-and-mortar brand of internet celebrity indicates that our proposed POI recommendation system can be used to conduct online promotion and purchasing to drive offline marketing and consumption.

Suggested Citation

  • Laisong Kang & Shifeng Liu & Daqing Gong & Mincong Tang, 2021. "A personalized point-of-interest recommendation system for O2O commerce," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(2), pages 253-267, June.
    • Handle: RePEc:spr:elmark:v:31:y:2021:i:2:d:10.1007_s12525-020-00416-5
    DOI: 10.1007/s12525-020-00416-5
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    References listed on IDEAS

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    Cited by:

    1. Arodh Lal Karn & Rakshha Kumari Karna & Bhavana Raj Kondamudi & Girish Bagale & Denis A. Pustokhin & Irina V. Pustokhina & Sudhakar Sengan, 2023. "RETRACTED ARTICLE: Customer centric hybrid recommendation system for E-Commerce applications by integrating hybrid sentiment analysis," Electronic Commerce Research, Springer, vol. 23(1), pages 279-314, March.
    2. Farah Tawfiq Abdul Hussien & Abdul Monem S. Rahma & Hala B. Abdulwahab, 2021. "An E-Commerce Recommendation System Based on Dynamic Analysis of Customer Behavior," Sustainability, MDPI, vol. 13(19), pages 1-21, September.
    3. Ravi S. Sharma & Aijaz A. Shaikh & Eldon Li, 2021. "Designing Recommendation or Suggestion Systems: looking to the future," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(2), pages 243-252, June.

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    More about this item

    Keywords

    POI recommendation system; O2O commerce; Internet celebrity economy; Location-based social networks; Heterogeneous information networks;
    All these keywords.

    JEL classification:

    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

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