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A Comparative Study of Collaborative Filtering and Content-Based Approaches for Improving the Accuracy of Travel Recommender Systems for Malayalam Language


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1 Department of Computer Science, Sullamussalam Science College, Affiliated to University of Calicut, India
 

In this paper, we present a personalized travel recommender system in the Malayalam language using artificial intelligence techniques. The study focuses on the use of travelogues and travel reviews written by travellers on social media as the primary source of data. A dataset of 11000 posts from 6444 travellers was collected from Facebook and other online platforms during 2020-2023. The data was pre-processed to extract relevant information such as travel mode, type of travel, location visited, and climate. Two approaches were used to build the recommender system: collaborative filtering-based K-means clustering and content-based hierarchical agglomerative clustering. The results of the study showed that the clustering approach significantly improved the efficiency and accuracy of the recommender system. K-means clustering achieved an accuracy of 91% and an F1 score of 85%, while the agglomerative hierarchical clustering approach achieved an accuracy of 85% and an F1 score of 84.25%. The results of this study demonstrate the potential of using travelogues and travel reviews to construct a personalized travel recommender system in regional languages.

Keywords

Personalized Travel Recommendation, Malayalam Language, Travelogues, K-Means Clustering, Agglomerative Clustering, Travel Reviews.
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  • A Comparative Study of Collaborative Filtering and Content-Based Approaches for Improving the Accuracy of Travel Recommender Systems for Malayalam Language

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Authors

Muneer V.K
Department of Computer Science, Sullamussalam Science College, Affiliated to University of Calicut, India
Mohamed Basheer K.P
Department of Computer Science, Sullamussalam Science College, Affiliated to University of Calicut, India

Abstract


In this paper, we present a personalized travel recommender system in the Malayalam language using artificial intelligence techniques. The study focuses on the use of travelogues and travel reviews written by travellers on social media as the primary source of data. A dataset of 11000 posts from 6444 travellers was collected from Facebook and other online platforms during 2020-2023. The data was pre-processed to extract relevant information such as travel mode, type of travel, location visited, and climate. Two approaches were used to build the recommender system: collaborative filtering-based K-means clustering and content-based hierarchical agglomerative clustering. The results of the study showed that the clustering approach significantly improved the efficiency and accuracy of the recommender system. K-means clustering achieved an accuracy of 91% and an F1 score of 85%, while the agglomerative hierarchical clustering approach achieved an accuracy of 85% and an F1 score of 84.25%. The results of this study demonstrate the potential of using travelogues and travel reviews to construct a personalized travel recommender system in regional languages.

Keywords


Personalized Travel Recommendation, Malayalam Language, Travelogues, K-Means Clustering, Agglomerative Clustering, Travel Reviews.

References