Title: Interpreted social graph traversal algorithms for enhanced recommendation precision and efficiency in computational applications

Authors: Jayanta Mondal; Sandipan Pine; Bijay Kumar Paikaray; Amita Yadav; Shalu Mehta

Addresses: School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, India ' Department of ECE, Centurion University of Technology and Management, Odisha, India ' Centre for Data Science, Department of Computer Science and Engineering, Siksha 'O' Anusandhan (Deemed to be) University, Odisha, India ' Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology, New Delhi, India ' Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology, New Delhi, India

Abstract: The social network recommendation algorithms focusing on two novel approaches: interpreted social breadth first search (BFS) and the interpreted social depth first search (DFS). These algorithms aim to enhance the recommendation process by leveraging insights from social network analysis, graph traversal techniques, and interpreted relevance measures. This investigation utilising a real dataset sourced from Amazon, we compare the performance of BFS and DFS against conventional recommendation methods such as item-based collaborative filtering and hybrid approaches. This research finding reveals that BFS and DFS not only exhibit commendable precision but also demonstrate superior efficiency in terms of runtime. Moreover, our analysis indicates that these algorithms effectively narrow down the search space within the dataset, contributing to computational savings. This report sheds light on the potential of integrating social network structures and Interpreted user profiles into recommendation systems, offering valuable insights for researchers and practitioners in the field of recommender systems.

Keywords: social network analysis; recommender systems; graph searching algorithms; user-based preferences; computational applications.

DOI: 10.1504/IJANS.2025.148931

International Journal of Applied Nonlinear Science, 2025 Vol.5 No.1, pp.42 - 56

Received: 26 Jun 2024
Accepted: 01 Oct 2024
Published online: 04 Oct 2025 *

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