Improve Quality of Recommendation System Using Hybrid Filtering Approach
- 10.2991/aer.k.211129.023How to use a DOI?
- recommender system; hybrid filtering; matrix factorization; collaborative filtering; content based filtering
Recommendation systems are widely used on website platforms such as e-commerce, marketplaces, streaming movies to produce appropriate item recommendations for each user. The traditional memory-based collaborative filtering approach is currently used in recommender systems. This approach relies on users’ item rating as a basic approach for calculates the similarity of users’ responses about products to predict item recommendations, but the weakness is high prediction errors. This study aimed to reduce prediction errors from a memory-based collaborative filtering approach using hybrid filtering, so the recommendation system’s quality can be improved. The hybrid filtering approach combined a collaborative filtering approach based on a matrix factorization model and content-based filtering, which can reduce prediction errors to produce accurate item recommendations. The proposed method has been evaluated ten times using the root mean squared error to measure the prediction error. As a result, the hybrid filtering approach produced the smallest prediction error of 0.68, while memory-based collaborative filtering was 2.98. Based on the results, the hybrid filtering approach is better than memory-based collaborative filtering.
- © 2021 The Authors. Published by Atlantis Press International B.V.
- Open Access
- This is an open access article under the CC BY-NC license.
Cite this article
TY - CONF AU - Annas Al Amin AU - Andi Sunyoto AU - Hanif Al Fatta PY - 2021 DA - 2021/11/30 TI - Improve Quality of Recommendation System Using Hybrid Filtering Approach BT - Proceedings of the International Conference on Innovation in Science and Technology (ICIST 2020) PB - Atlantis Press SP - 103 EP - 106 SN - 2352-5401 UR - https://doi.org/10.2991/aer.k.211129.023 DO - 10.2991/aer.k.211129.023 ID - AlAmin2021 ER -