Abstract
Location-based services encompass a spectrum of services. Today, it is easier to locate or search for our favorite restaurant, shop, etc., under these services. It helps us get access to important and up-to-date information about their surroundings on a single tap. This research proposes two location-based recommendation systems by using the collaborative and content-based filtering recommendation techniques. The first one is a personalized location-based recommender that uses the content filtering technique. In this recommender, the behavioral patterns are extracted from the user’s location history and then provide personalized recommendations based on patterns. Apriori algorithm has been used to extract user-specific behavioral patterns based on time zone, weekday, and location type. The second one is a generalized location-based recommender that uses the collaborative filtering technique. It employs the K-means clustering algorithm and the silhouette metric and elbow method to find the optimal index K (clusters).
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Gupta, R., Pandey, I., Mishra, K., Seeja, K.R. (2022). Recommendation System for Location-Based Services. In: Iyer, B., Ghosh, D., Balas, V.E. (eds) Applied Information Processing Systems . Advances in Intelligent Systems and Computing, vol 1354. Springer, Singapore. https://doi.org/10.1007/978-981-16-2008-9_52
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DOI: https://doi.org/10.1007/978-981-16-2008-9_52
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