Abstract
Location-based social networks (LBSNs) have impacted our lives recently to a great deal. Typical location-based social networking sites have provision of declaring a check-in at a venue for users. Users can communicate this information with their friends, thus, generating huge dataset. This large geographical information has paved the way to build location-based recommender systems. Location recommendation service is an integral feature of location-based social networks. More and more users are using this service to explore new places and take timely and effective decisions. These systems provide a rich knowledge about a new place that a user has never visited before and also recommend interesting locations to the user after mining socio-spatial check-in data. In this paper, the authors present non-personalized techniques to utilize the check-in information for recommending popular and interesting locations to users. Background of location-based social networks and various techniques to develop location recommender system is discussed initially, followed by existing work and research issues of location-based recommender system. Authors have presented illustrative examples to mine the available spatial information of real-world location-based social network to suggest best interesting locations.
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Behl, R., Kashyap, I. (2023). Learning Impact of Non-personalized Approaches for Point of Interest Recommendation. In: Dutta, P., Bhattacharya, A., Dutta, S., Lai, WC. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 1348. Springer, Singapore. https://doi.org/10.1007/978-981-19-4676-9_60
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