@inproceedings{bdbd389f2f044050a3a890406afada90,
title = "Two-steps graph-based collaborative filtering using user and item similarities: Case study of E-commerce recommender systems",
abstract = "Collaborative filtering has been used extensively in the commercial recommender system because of its effectiveness and ease of implementation. Collaborative filtering predicts a user's preference based on preferences of similar users or from similar items to items that are purchased by this user. The use of either user-based or item-based similarity is not sufficient. For that particular issues, hybridization of user-based and item-based in one collaborative filtering recommender system can be used to sort relevant item out of a set of candidates. This method applies similarity measures using link prediction to predict target item by combining user similarity with item similarity. The experiment results show that the combination of user and item similarities in two-steps collaborative filtering setting improves accuracy compared to the algorithm applying only user or item similarity.",
author = "Putra, {Aghny Arisya} and Rahmad Mahendra and Indra Budi and Qorib Munajat",
note = "Funding Information: The authors gratefully acknowledge the support of the “Hibah Publikasi Internasional Terindeks untuk Tugas Akhir Mahasiswa tahun anggaran 2017” (PITTA UI Grant 2017) Contract No. 406/UN2.R3.1/HKP.05.00/2017 Publisher Copyright: {\textcopyright} 2017 IEEE.; 4th International Conference on Data and Software Engineering, ICoDSE 2017 ; Conference date: 01-11-2017 Through 02-11-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/ICODSE.2017.8285891",
language = "English",
series = "Proceedings of 2017 International Conference on Data and Software Engineering, ICoDSE 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--6",
booktitle = "Proceedings of 2017 International Conference on Data and Software Engineering, ICoDSE 2017",
address = "United States",
}