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Popularised Similarity Function for Effective Collaborative Filtering Recommendations

Popularised Similarity Function for Effective Collaborative Filtering Recommendations

Abba Almu, Abubakar Roko, Aminu Mohammed, Ibrahim Saidu
Copyright: © 2020 |Volume: 10 |Issue: 1 |Pages: 14
ISSN: 2155-6377|EISSN: 2155-6385|EISBN13: 9781799807315|DOI: 10.4018/IJIRR.2020010103
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MLA

Almu, Abba, et al. "Popularised Similarity Function for Effective Collaborative Filtering Recommendations." IJIRR vol.10, no.1 2020: pp.34-47. http://doi.org/10.4018/IJIRR.2020010103

APA

Almu, A., Roko, A., Mohammed, A., & Saidu, I. (2020). Popularised Similarity Function for Effective Collaborative Filtering Recommendations. International Journal of Information Retrieval Research (IJIRR), 10(1), 34-47. http://doi.org/10.4018/IJIRR.2020010103

Chicago

Almu, Abba, et al. "Popularised Similarity Function for Effective Collaborative Filtering Recommendations," International Journal of Information Retrieval Research (IJIRR) 10, no.1: 34-47. http://doi.org/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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