Evaluation of Partitioning Clustering Algorithms for Processing Social Media Data in Tourism Domain

Recommender systems are evolving as an essential part of every industry with no exception to travel and tourism segment. Considering the exponential increase in social media usage and huge volume of data being generated through this channel, it can be considered as a vital source of input data for m...

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Bibliographic Details
Published in:2018 IEEE Recent Advances in Intelligent Computational Systems (RAICS) pp. 127 - 131
Main Authors: Renjith, Shini, Sreekumar, A., Jathavedan, M.
Format: Conference Proceeding
Language:English
Japanese
Published: IEEE 01.12.2018
Subjects:
Cluster Evaluation
Clustering algorithms
Extraterrestrial measurements
Linear programming
Machine learning algorithms
Partitioning algorithms
Recommender systems
Social networking (online)
Travel and Tourism
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Summary:Recommender systems are evolving as an essential part of every industry with no exception to travel and tourism segment. Considering the exponential increase in social media usage and huge volume of data being generated through this channel, it can be considered as a vital source of input data for modern recommender systems. This in turn resulted in the need of efficient and effective mechanisms for contextualized information retrieval. Traditional recommender systems adopt collaborative filtering techniques to deal with social context. However they turn out to be computational intensive and thereby less scalable with internet and social media as input channel. A possible solution is to adopt clustering techniques to limit the data to be considered for recommendation process. In tourism context, based on social media interactions like reviews, forums, blogs, feedbacks, etc. travelers can be clustered to form different interest groups. This experimental analysis aims at comparing key clustering algorithms with the aim of finding an optimal option that can be adopted in tourism domain by applying social media datasets from travel and tourism context.
DOI:10.1109/RAICS.2018.8635080