A scalable model for real‐time venue recommendations using MapReduce

Summary The popular social networks such as Facebook, Twitter, and Foursquare closely monitor user activities to recommend different services and events. Among others, venue recommendation proposes users the most appropriates venues based on the user preferences. It offers facility to the user to ma...

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Bibliographic Details
Published in:Concurrency and computation 2020-11, Vol.32 (21), p.n/a
Main Authors: Rehman, Faisal, Khalid, Osman, Haq, Nuhman ul, Maqsood, Tahir, Ali, Mazhar, Ahmad, Raja Wasim, Shuja, Junaid, Sarwar, Shahzad, Madani, Sajjad Ahmad
Format: Article
Language:English
Subjects:
Cloud computing
Collaboration
Filtration
K‐means clustering
K‐nearest neighbor
MapReduce
Model accuracy
Physical factors
recommendation systems
Recommender systems
Social networks
Unstructured data
Venue
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Summary:Summary The popular social networks such as Facebook, Twitter, and Foursquare closely monitor user activities to recommend different services and events. Among others, venue recommendation proposes users the most appropriates venues based on the user preferences. It offers facility to the user to mark the check‐ins when a venue is visited. The traditional venue recommendation systems have opted collaborative filtering to propose recommendations. However, collaborative filtering overlooked certain critical issues, including real‐time recommendations, cold start, and scalability, for venue recommendations. Moreover, real‐time physical factors such as distance from the venue are also not considered in traditional venue recommendation systems. Furthermore, parsing and processing of huge volume of unstructured data is the main challenge for conventional recommender systems, particularly when dealing with real‐time recommendations. For efficient scaling, significant computational and storage resources for recommendation systems are desired. This article proposes a Real‐Time Venue Recommendation (RTVR) model that utilizes cloud‐based MapReduce framework to process, compare, mine, and manage large data sets for generating recommendations. The results showed that the proposed model has improved accuracy for real‐time recommendations. The proposed RTVR is more scalable as it exploits a cloud‐based architecture. Moreover, the proposed techniques are verified using formal verification methods.
ISSN:1532-0626
1532-0634
DOI:10.1002/cpe.5597