Proceedings of the 1st International Seminar on Teacher Training and Education, ISTED 2021, 17-18 July 2021, Purwokerto, Indonesia

Research Article

User-Based Collaborative Filtering Using Agglomerative Clustering on Recommender System

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  • @INPROCEEDINGS{10.4108/eai.17-7-2021.2312410,
        author={Malim  Muhammad},
        title={User-Based Collaborative Filtering Using Agglomerative Clustering on Recommender System},
        proceedings={Proceedings of the 1st International Seminar on Teacher Training and Education, ISTED 2021, 17-18 July 2021, Purwokerto, Indonesia},
        publisher={EAI},
        proceedings_a={ISTED},
        year={2021},
        month={10},
        keywords={recommended system user-based collaborative filtering agglomerative clustering},
        doi={10.4108/eai.17-7-2021.2312410}
    }
    
  • Malim Muhammad
    Year: 2021
    User-Based Collaborative Filtering Using Agglomerative Clustering on Recommender System
    ISTED
    EAI
    DOI: 10.4108/eai.17-7-2021.2312410
Malim Muhammad1,*
  • 1: Universitas Muhammadiyah Purwokerto
*Contact email: malim.muhammad@gmail.com

Abstract

Content-based, collaborative filtering, demographic, knowledge-based, and hybrid recommender systems are the five categories of recommendation systems. User-based collaborative filtering and item-based collaborative filtering are the two types of collaborative filtering. However, the user-based approaches can be claimed to represent the user; researchers will employ them here. This method is more concerned with the user's likeness, or similarity than with the user's evaluated item. The accuracy of user-based collaborative filtering approaches employing agglomerative Clustering with similarity computations, i.e., cosine similarity, is improved in this study. MovieLens (https://grouplens.org/datasets/movielens/) provided the researchers with the data they needed. Between January 9, 1995, and October 16, 2016, a total of 100004 ratings for 9,125 films were collected from 671 individuals. At least 20 movies have been rated by each user. Each rating has a value of 1 to 5. The data utilized for testing is five value data from each user. In other words, 3,355 data points were tested in total. Using the single linkage clustering approach to cluster films in the use-based method has been shown to improve the accuracy of results that differ significantly between scenarios one and two, namely 3,409 and 3.26. MAE and RMSE are the accuracy gauges utilized in the analysis, and the smaller the value (closer to zero), the better the program results.