From Cluster Ranking to Document Ranking

Egor Markovskiy, Fiana Raiber, Shoham Sabach, Oren Kurland

نتاج البحث: فصل من :كتاب / تقرير / مؤتمرمنشور من مؤتمرمراجعة النظراء

ملخص

The common approach of using clusters of similar documents for ad hoc document retrieval is to rank the clusters in response to the query; then, the cluster ranking is transformed to document ranking. We present a novel supervised approach to transform cluster ranking to document ranking. The approach allows to simultaneously utilize different clusterings and the resultant cluster rankings; this helps to improve the modeling of the document similarity space. Empirical evaluation shows that using our approach results in performance that substantially transcends the state-of-the-art in cluster-based document retrieval.

اللغة الأصليةالإنجليزيّة
عنوان منشور المضيفSIGIR 2022 - Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
الصفحات2137-2141
عدد الصفحات5
رقم المعيار الدولي للكتب (الإلكتروني)9781450387323
المعرِّفات الرقمية للأشياء
حالة النشرنُشِر - 6 يوليو 2022
الحدث45th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2022 - Madrid, أسبانيا
المدة: ١١ يوليو ٢٠٢٢١٥ يوليو ٢٠٢٢

سلسلة المنشورات

الاسمSIGIR 2022 - Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

!!Conference

!!Conference45th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2022
الدولة/الإقليمأسبانيا
المدينةMadrid
المدة١١/٠٧/٢٢١٥/٠٧/٢٢

All Science Journal Classification (ASJC) codes

  • !!Computer Graphics and Computer-Aided Design
  • !!Information Systems
  • !!Software

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