Widiyaningtyas T., Hidayah I., Adji T.B.
Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia; Department of Electrical Engineering, Universitas Negeri Malang, Malang, Indonesia
Collaborative filtering is one of the most widely used recommendation system approaches. One issue in collaborative filtering is how to use a similarity algorithm to increase the accuracy of the recommendation system. Most recently, a similarity algorithm that combines the user rating value and the user behavior value has been proposed. The user behavior value is obtained from the user score probability in assessing the genre data. The problem with the algorithm is it only considers genre data for capturing user behavior value. Therefore, this study proposes a new similarity algorithm – so-called User Profile Correlation-based Similarity (UPCSim) – that examines the genre data and the user profile data, namely age, gender, occupation, and location. All the user profile data are used to find the weights of the similarities of user rating value and user behavior value. The weights of both similarities are obtained by calculating the correlation coefficients between the user profile data and the user rating or behavior values. An experiment shows that the UPCSim algorithm outperforms the previous algorithm on recommendation accuracy, reducing MAE by 1.64% and RMSE by 1.4%. © 2021, The Author(s).
Collaborative filtering; UPCSim; User behavior value; User rating value
Journal of Big Data
Publisher: Springer Science and Business Media Deutschland GmbH
Volume 8, Issue 1, Art No 52, Page – , Page Count
Journal Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103547164&doi=10.1186%2fs40537-021-00425-x&partnerID=40&md5=51dd798b34e61cc2cf867fe23f115862
Type: All Open Access, Gold
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