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Point-of-Interests Recommendation Service in Location-Based Social Networks: A Survey, Research Challenges, and Future Perspectives

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Book cover Sustainable Smart Cities

Part of the book series: Studies in Computational Intelligence ((SCI,volume 942))

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Abstract

The focus on accurate Point-Of-Interest (POI) recommendation, specifically in location-based services (LBS), has gained all social-network developers’ attention. This is because the POI service has a significant role in helping users to locate targeted areas, including hospitals, airports, stations, billing addresses, post-office, shopping-mall, and other POIs. Equally, many attempts have been realized to provide accurate POI recommendation solutions via commercial and academic sectors. However, the recommendation solutions have their weaknesses and abilities in terms of initial check-ins, accuracy, behavior of the users’ activities, and historical passed locations. According to the state-of-the-art, a survey of such solutions and utilized techniques is needed. Therefore, this paper aims to address most of the currently proposed solutions and implemented techniques for offering accurate POI recommendation systems. Further, this paper also presents a taxonomy of POI recommendation solutions in which the solutions are classified into content-based filtering, collaborative-based filtering, and hybrid-based filtering solutions. This is with a particular focus on the details of the implemented techniques/algorithms and utilized features. Providing an accurate POIs recommendation solution and other related issues are listed as future research attempts.

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Author information

Authors and Affiliations

  1. Department of Information System Engineering Techniques, Erbil Technical, Engineering College, Erbil Polytechnic University, Erbil, Kurdistan Region-F.R., Iraq

    Safar Maghdid Asaad

  2. Department of Software Engineering, Faculty of Engineering, Koya University, University Park, Koya, Erbil, Kurdistan Region-F.R., Iraq

    Safar Maghdid Asaad

  3. Department of Computer Science, Knowledge University, University Park, Kirkuk Road, 44001, Erbil, Iraq

    Kayhan Zrar Ghafoor

  4. Department of Software Engineering, Faculty of Engineering, Koya University, Koysinjaq, Kurdistan Region-F.R., Iraq

    Halgurd Sarhang

  5. Department of Computer Science and Information Systems Saginaw, Valley State University, Allendale, MI, USA

    Aos Mulahuwaish

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  1. Safar Maghdid Asaad
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  2. Kayhan Zrar Ghafoor
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  3. Halgurd Sarhang
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  4. Aos Mulahuwaish
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Correspondence to Aos Mulahuwaish .

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Editors and Affiliations

  1. Narsee Monjee Institute of Management Studies (NMIMS), School of Technology Management and Engineering, Chandigarh Campus, India

    Pradeep Kumar Singh

  2. Polish Academy of Sciences, Warsaw, Poland

    Marcin Paprzycki

  3. ENSIAS College of Engineering, Mohammed V University, Rabat, Morocco

    Mohamad Essaaidi

  4. Department of Computer Science and Engineering, Mississippi State University, Starkville, MS, USA

    Shahram Rahimi

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Asaad, S.M., Ghafoor, K.Z., Sarhang, H., Mulahuwaish, A. (2023). Point-of-Interests Recommendation Service in Location-Based Social Networks: A Survey, Research Challenges, and Future Perspectives. In: Singh, P.K., Paprzycki, M., Essaaidi, M., Rahimi, S. (eds) Sustainable Smart Cities. Studies in Computational Intelligence, vol 942. Springer, Cham. https://doi.org/10.1007/978-3-031-08815-5_4

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