A collaborative filtering recommendation framework utilizing social networks
<p dir="ltr">Collaborative filtering is a widely used technique for providing personalized recommendations to users. However, traditional collaborative filtering methods fail to consider the social connections between users. The current study proposes a collaborative filtering recomm...
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2023
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| Summary: | <p dir="ltr">Collaborative filtering is a widely used technique for providing personalized recommendations to users. However, traditional collaborative filtering methods fail to consider the social connections between users. The current study proposes a collaborative filtering recommendation framework that employs social networks to generate more precise and pertinent recommendations. The framework is based on a modified version of the user-based collaborative filtering algorithm, which computes user similarity based on their ratings and social connections. The similarity measure is determined by a weighted combination of these two factors, with the weights learned through an optimization process. The framework is evaluated using a dataset of movie ratings and social connections between users. The findings reveal that the proposed approach outperforms traditional collaborative filtering methods regarding recommendation accuracy and relevance. Moreover, the framework can offer more diverse recommendations compared to traditional methods. In summary, the proposed framework integrates social networks to enhance the accuracy and relevance of collaborative filtering recommendations. The approach has various applications, including e-commerce, music, and movie recommendation, and can potentially address the issues of cold-start and sparsity in collaborative filtering.</p><h2>Other Information</h2><p dir="ltr">Published in: Machine Learning with Applications<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.mlwa.2023.100495" target="_blank">https://dx.doi.org/10.1016/j.mlwa.2023.100495</a></p> |
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