Alleviating Unwanted Recommendations Issues in Collaborative Filtering Based Recommender Systems

Alleviating Unwanted Recommendations Issues in Collaborative Filtering Based Recommender Systems

Автор: Abba Almu, Abubakar Roko

Журнал: International Journal of Engineering and Manufacturing @ijem

Статья в выпуске: 2 vol.14, 2024 года.

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The overabundance of information on the internet and ecommerce has resulted to the development of recommender system to discover interesting items or contents that are recommendable to the user. The recommended items might be of no interest or unwanted to the users and can make users to lose interest in the recommendations. In this work, a Collaborative Filtering (CF) based method which exploits the initial top-N recommendation lists of an item-based CF algorithm based on unwanted recommendations penalisation is presented. The method utilises a relevance feedback mechanism to solicit for users preferences on the recommendations while popularise similarity function minimises the chances of recommending unwanted items. The work explains the proposed algorithm in detail and demonstrates the improvements required on existing CF to provide some adjustments required to improve subsequent recommendations to users.

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Recommender System, Collaborative Filtering, Unwanted Recommendations, Relevance Feedback, Popularise Similarity Function

Короткий адрес: https://sciup.org/15019327

IDR: 15019327   |   DOI: 10.5815/ijem.2024.02.04

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