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Link Prediction based on bipartite graph for recommendation system using optimized SVD++ by Anshul Gupta; Pravin Shrinath is a Computer Science article available to read on EtoBox.

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To alleviate the big data difficulties that have created a potential problem for many Internet users, it is necessary to filter, rank, and efficiently communicate the relevant information on the Web, where the diversity of possibilities is overwhelming. Recommender systems, which sift through enormous amounts of dynamically generated data to give consumers with personalised information and services, are able to overcome this problem. Bipartite graphs are currently generally used to store and understand this data due to its sparse nature. Data are mapped to a bipartite user-item interaction network where the graph topology captures detailed information about user-item associations, transforming a recommendation issue into a link prediction problem. Earlier, approaches for link prediction in bipartite graphs for various recommendation systems were developed, but the efficacy of the prediction methodology was not close to the standards required by real-time recommenders. So, the primary goal of this research is to offer an effective link prediction-based recommendation system that takes advantage of SVD++ along with K-nearest neighbors and reduces the system's error rate, leading to b

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Author

Anshul Gupta; Pravin Shrinath

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)