ITERATIVE RATING THROUGH VOTING ALGORITHM-MULTI PARAMETER AGGREGATION

With the rapid growth of academic search engines, the visibility and impact of scholarly articles depend heavily on how effectively they are ranked. While systems such as Google Scholar emphasize citation counts, community sentiment and other contextual attributes can also provide important signals of trustworthiness. Similarly, in professional platforms like LinkedIn, the credibility of recommendations depends not only on the recommender’s opinion but also on their expertise and standing within the community. This paper introduces an Iterative Voting Algorithm with Multi-Parameter Aggregation (VMPA) that integrates multiple voter attributes into the ranking process. The algorithm is designed to mitigate individual collusion, group collusion, and biased voting, while ensuring that authentic but infrequent voters are not overlooked. Each voter is associated with measurable parameters such as academic experience, citation record, and organizational affiliation, which are incorporated into their voting weight. The method is evaluated using both real and synthetic datasets in the context of conference ranking. Results show that the algorithm successfully identifies colluders, preserves genuine contributions, and produces rankings consistent with credible benchmarks such as CORE. The findings suggest that multi-parameter aggregation improves robustness, enhances trustworthiness, and reduces the number of iterations required for convergence