Fitri Marisa, Sharifah Sakinah Syed Ahmad, Zeratul Izzah Mohd Yusoh, Tubagus Mohammad Akhriza, Wiwin Purnomowati, Rakesh Kumar Pandey
Challenge in developing a collaborative filtering (CF)-based recommendation system is the problem of cold-starting of items that causes the data to sparse and reduces the accuracy of the recommendations. Therefore, to produce high accuracy a match is needed between the types of data and the approach used. Two approaches in CF include user-based and item-based CFs, both of which can process two types of data; implicit and explicit data. This work aims to find a combination of approaches and data types that produce high accuracy. Cosine-similarity is used to measure the similarity between users and also between items. Mean Absolute Error is also measured to discover the accuracy of a recommendation. Testing of three groups of data based on sparseness results in the best accuracy in an explicit data-based approach that has the smallest MAE value. The result is that the average MAE value for user based (implicit data) is 0.1032, user based (explicit data) is 0.2320, item based (implicit data) is 0.3495, and item based (explicit data) is 0.0926. The best accuracy is in the item-based (explicit-data) approach which is the smallest average MAE value. © 2019 International Journal of Advanced Computer Science and Applications.
Informatics Engineering, University of Widyagama, Jl. Borobudur no. 35, Malang, Indonesia; Department of Science Technology (DST) under AIRTF Program -RTF/2018/000033, India; Centre for Advanced Computing Technology, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Jalan Hang Tuah Jaya, Durian Tunggal, Melaka, 76100, Malaysia; Pradnya Paramita School of Informatics and Computer, Jl. LA Sucipto no. 249A, Malang, Indonesia; University of Widyagama, Jl. Borobudur no. 35, Malang, Indonesia; Kirori Mal College, University of Delhi, North campus Delhi, 110007, India
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