Metasearch aggregation using linear programming and neural networks

Sujeet Sharma, Srikrishna Govindaluri, Gholam R. Amin*

*المؤلف المقابل لهذا العمل

نتاج البحث: المساهمة في مجلةArticleمراجعة النظراء

1 اقتباس (Scopus)


A metasearch engine aggregates the retrieved results of multiple search engines for a submitted query. The purpose of this paper is to formulate a metasearch aggregation using linear programming and neural networks by incorporating the importance weights of the involved search engines. A two-stage methodology is introduced where the importance weights of individual search engines are determined using a neural network model. The weights are then used by a linear programming model for aggregating the final ranked list. The results from the proposed method are compared with the results obtained from a simple model that assumes subjective weights for search engines. The comparison of the two sets of results shows that neural network-based linear programming model is superior in optimising the relevance of aggregated results.

اللغة الأصليةEnglish
الصفحات (من إلى)351-366
عدد الصفحات16
دوريةInternational Journal of Operational Research
مستوى الصوت33
رقم الإصدار3
المعرِّفات الرقمية للأشياء
حالة النشرPublished - 2018

ASJC Scopus subject areas

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