Hybrid Cluster based Collaborative Filtering using Firefly and Agglomerative Hierarchical clustering
Dublin Core
Title
Hybrid Cluster based Collaborative Filtering using Firefly and Agglomerative Hierarchical clustering
Subject
Machine learning; Recommendation systems; Meta-heuristic optimization
Description
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.
Creator
Spoorthy G, Sriram G. Sanjeevi
Source
www.ijcit.com
Date
December 2021
Contributor
peri irawan
Format
pdf
Language
english
Type
text
Files
Collection
Citation
Spoorthy G, Sriram G. Sanjeevi, “Hybrid Cluster based Collaborative Filtering using Firefly and Agglomerative Hierarchical clustering,” Repository Horizon University Indonesia, accessed June 1, 2025, https://repository.horizon.ac.id/items/show/9014.