A Two-Step Approach to Weighted Bipartite Link Recommendations

Many real-world person-person or person-product relationships can be modeled graphically. Specifically, bipartite graphs are especially useful when modeling scenarios involving two disjoint groups. As a result, existing papers have utilized bipartite graphs to address the classical link recommendati...

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Bibliographic Details
Published in:International journal of advanced computer science & applications 2022-01, Vol.13 (12)
Main Author: Ma, Nathan
Format: Article
Language:English
Subjects:
Algorithms
Binary system
Collaboration
Computer science
Datasets
Graphs
Neural networks
Product reviews
Online Access:Get full text
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Summary:Many real-world person-person or person-product relationships can be modeled graphically. Specifically, bipartite graphs are especially useful when modeling scenarios involving two disjoint groups. As a result, existing papers have utilized bipartite graphs to address the classical link recommendation problem. Applying the principle of bipartite graphs, this research presents a modified approach to this problem which employs a two-step algorithm for making recommendations that accounts for the frequency and similarity between common edges. Implemented in Python, the new approach was tested using bipartite data from Epinions and Movielens data sources. The findings showed that it improved the baseline results, performing within an estimated error of 14 per cent. This two-step algorithm produced promising findings, and can be refined to generate recommendations with even greater accuracy.
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2022.0131201