An ecommerce recommendation algorithm based on link prediction

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.