Hybrid Cluster based Collaborative Filtering using Firefly and Agglomerative Hierarchical Clustering
DOI:
https://doi.org/10.24203/ijcit.v10i6.170Keywords:
Recommendation Systems, Clustering Techniques, Firefly AlgorithmAbstract
Recommendation Systems finds the user preferences based on the purchase history of an individual using data mining and machine learning techniques. To reduce the time taken for computation Recommendation systems generally use a pre-processing technique which in turn helps to increase high low performance and over comes over-fitting of data. In this paper, we propose a hybrid collaborative filtering algorithm using firefly and agglomerative hierarchical clustering technique with priority queue and Principle Component Analysis (PCA). We applied our hybrid algorithm on movielens dataset and used Pearson Correlation to obtain Top N recommendations. Experimental results show that the our algorithm delivers accurate and reliable recommendations showing high performance when compared with existing algorithms.
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Copyright (c) 2021 Spoorthy G., Sriram G. Sanjeevi
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The articles published in International Journal of Computer and Information Technology (IJCIT) is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
