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@article{168064,
        author = {Dr. Swati Kishor Borse},
        title = {Comparative Evaluation of Actual and Predictive Rankings from Major Search Engine Results},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {4},
        pages = {1633-1641},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=168064},
        abstract = {Evaluation of search engines relies on analysis of search results for selected test queries. From these results, basically conclusions can be drawn in terms of relevance of the results for users. In practice however, evaluation can be determined by analyzing the results of major search engines. This paper presents and analyzes the predicted relevance system, which allows predicting a rank of search results according to relevancy for given query based on number of features found and score generated for the retrieved search results of major search engines. Given a set of retrieved search results, our proposed  system finds out scores for search results and moves down non-relevant one having less number of score. Score computed directly correlates with actual relevancy judgment. The use of its relevance in actual evaluation scenarios is illustrated on web page results retrieved from major search engines for different queries. Predicted relevancy of retrieved ranking results of Google search engine improved from 88% to 99%, Ask improved from 90% to 99%, Bing improved from 93% to 98%, Gigablast improved from 89% to 98%  while Yahoo improved from 93% to 100%.},
        keywords = {Predict, relevancy, ranking, search engine.},
        month = {November},
        }