Debashish Roy, Rajarshi Roy Chowdhury, Abdullah Bin Nasser, Afdhal Azmi, Marzieh Babaeianjelodar
Matrix factorization (MS) is a collaborative filtering (CF) based approach, which is widely used for recommendation systems (RS). In this research work, we deal with the content recommendation problem for users in a content management system (CMS) based on users’ feedback data. The CMS is applied for publishing and pushing curated content to the employees of a company or an organization. Here, we have used the users’ feedback data and content data to solve the content recommendation problem. We prepare individual user-profiles and then generate recommendation results based on different categories, including Direct Interaction, Social Share and Reading Statistics, of user’s feedback data. Subsequently, we analyze the effect of the different categories on the recommendation results. The results have shown that different categories of feedback data have different impacts on recommendation accuracy. The best performance achieves if we include all types of data for the recommendation task. We also incorporate content similarity as a regularization term into an MF model for designing a hybrid model. Experimental results have shown that the proposed hybrid model demonstrates better performance compared with the traditional MF-based models. © 2023 American Institute of Physics Inc.. All rights reserved.
Information Technology Management, Ryerson University, 350 Victoria Street, Toronto, M5B 2K3, ON, Canada; Faculty of Integrated Technologies, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong, BE1410, Brunei Darussalam; Department of Computer Science and Engineering, Sylhet International University, Shamimabad Road, Sylhet, 3100, Bangladesh; School of Technology and Innovation, University of Vaasa, Vaasa, FI-65200, Finland; Department of Electrical and Computer Engineering, Faculty of Engineering, Universitas Syiah Kuala, Banda Aceh, Indonesia; Department of Computer Science, Clarkson University, New York, United States
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