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

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.