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Popularised Similarity Function for Effective Collaborative Filtering Recommendations
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Author(s): Abba Almu (Department of Mathematics, Computer Science Unit, Usmanu Danfodiyo University, Sokoto, Nigeria), Abubakar Roko (Usmanu Danfodiyo University, Sokoto, Nigeria), Aminu Mohammed (Department of Mathematics, Computer Science Unit, Usmanu Danfodiyo University, Sokoto, Nigeria)and Ibrahim Saidu (Department of Information and Communication Technology, Faculty of Engineering and Environmental Desi, Sokoto, Nigeria)
Copyright: 2020
Volume: 10
Issue: 1
Pages: 14
Source title:
International Journal of Information Retrieval Research (IJIRR)
Editor(s)-in-Chief: Zhongyu Lu (University of Huddersfield, UK)
DOI: 10.4018/IJIRR.2020010103
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Abstract
The existing similarity functions use the user-item rating matrix to process similar neighbours that can be used to predict ratings to the users. However, the functions highly penalise high popular items which lead to predicting items that may not be of interest to active users due to the punishment function employed. The functions also reduce the chances of selecting less popular items as similar neighbours due to the items with common ratings used. In this article, a popularised similarity function (pop_sim) is proposed to provide effective recommendations to users. The pop_sim function introduces a modified punishment function to minimise the penalty on high popular items. The function also employs a popularity constraint which uses ratings threshold to increase the chances of selecting less popular items as similar neighbours. The experimental studies indicate that the proposed pop_sim is effective in improving the accuracy of the rating prediction in terms of not only lowering the MAE but also the RMSE.
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