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An ecommerce recommendation algorithm based on link prediction
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DOI：10.1016/j.aej.2021.04.081.png)
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摘要
En 中文
In the field of ecommerce, most recommendation algorithms are based on user-item bipartite graph network (BGN). But this kind of recommendation algorithm is severely lacking in accuracy and diversity. In this paper, a novel ecommerce recommendation algorithm is proposed based on BGN link prediction. Firstly, all the user-item data were imported into distance formula to calculate the similarity between the attributes. Then, the BGN was projected into a single-mode net-work (SMN), making it more efficient to extract potential links from the BGN. On this basis, the potential links were predicted based on similarity. Through experiments on real ecommerce data-sets, it was proved that our algorithm has a higher accuracy and coverage than typical recommen-dation algorithms. (c) 2021 THE AUTHOR. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University.
Keyword:
Recommendation algorithm
Bipartite graph network (BGN)
Link prediction
Ecommerce
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