Triyanna Widiyaningtyas, Didik Dwi Prasetya, Heru Wahyu Herwanto
Neighbor-based collaborative filtering is one of the prevalent recommendation approaches that apply similarity algorithms. Using a similarity algorithm to improve the accuracy of the recommendation system is one challenge in collaborative filtering. The computation of similarity now heavily relies on user rating and behavior scores. Users' preferences for each product kind (genre) demonstrate the user behavior’s value. The algorithm's weakness is that it only considers genre data when estimating user behavior, regardless of when the user rated the item. Therefore, this study aims to develop a new similarity algorithm by considering the genre and the time weight of the item rating, which is called time loss function-based similarity (TLFSim). Newly assessed items have a higher weight than those estimated for a long time. Our experiment tested the TLFSim’s performance compared to the user-score-probability-collaborative-filtering (UPCF) algorithm using the MovieLens 100k dataset. The experimental results demonstrate that the TLFSim algorithm surpasses the prior approach regarding recommendation accuracy, reducing mean absolute error (MAE) by 8.28% and root mean square error (RMSE) by 4.57%. © (2023), (Intelligent Network and Systems Society). All Rights Reserved.
Department of Electrical Engineering and Informatics, Universitas Negeri Malang, Indonesia
