- Research Scholar, Department of Computer Science and Engineering, Sant Baba Bhag Singh University, Jalandhar, Punjab, India
- Assistant Professor, PG Department of Computer Science and Applications, Trai Shatabdi Guru Gobind Singh Khalsa College, Amritsar, Punjab, India
Link prediction is a challenging task in recommender systems, as it requires the ability to accurately predict future links between users and items. In this study, we propose a novel hybrid link prediction algorithm for e-commerce recommender systems that combines the common neighbor and resource allocation methods. The common neighbor method is a straightforward and intuitive algorithm that calculates the number of shared neighbors between two nodes. The intuition is that if two nodes have many common neighbors, they are more likely to be linked in the future. The resource allocation method is a more sophisticated algorithm that weights the common neighbors based on their importance. The intuition is that if two nodes have common neighbors that are themselves well-connected, then the two nodes are more likely to be linked. Our proposed hybrid algorithm combines the strengths of the common neighbor and resource allocation methods. We first calculate the common neighbor score between each pair of users and items. Afterward, the common neighbor score is adjusted by considering the significance or importance of the shared neighbors. The importance of a common neighbor is calculated using a resource allocation algorithm.
Keywords: Resource allocation algorithm, novel hybrid link prediction algorithm, recommender systems, ROC curve, AUC curve, sophisticated algorithm
[This article belongs to International Journal of Data Structure Studies(ijdss)]
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|Received||July 19, 2023|
|Accepted||July 24, 2023|
|Published||August 16, 2023|