-
Fuhr, N.: Ranking-Experimente mit gewichteter Indexierung (1986)
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- Date
- 14. 6.2015 22:12:44
- Source
- Deutscher Dokumentartag 1985, Nürnberg, 1.-4.10.1985: Fachinformation: Methodik - Management - Markt; neue Entwicklungen, Berufe, Produkte. Bearb.: H. Strohl-Goebel
-
Weller, K.; Stock, W.G.: Transitive meronymy : automatic concept-based query expansion using weighted transitive part-whole relations (2008)
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- Source
- Information - Wissenschaft und Praxis. 59(2008) H.3, S.165-170
-
Behnert, C.; Plassmeier, K.; Borst, T.; Lewandowski, D.: Evaluierung von Rankingverfahren für bibliothekarische Informationssysteme (2019)
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- Source
- Information - Wissenschaft und Praxis. 70(2019) H.1, S.14-23
-
Chang, C.-H.; Hsu, C.-C.: Integrating query expansion and conceptual relevance feedback for personalized Web information retrieval (1998)
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- Date
- 1. 8.1996 22:08:06
-
Kanaeva, Z.: Ranking: Google und CiteSeer (2005)
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- Date
- 20. 3.2005 16:23:22
- Source
- Information - Wissenschaft und Praxis. 56(2005) H.2, S.87-92
-
Fan, W.; Fox, E.A.; Pathak, P.; Wu, H.: ¬The effects of fitness functions an genetic programming-based ranking discovery for Web search (2004)
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- Date
- 31. 5.2004 19:22:06
-
Sparck Jones, K.: Search term relevance weighting given little relevance information (1979)
0.02
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- Footnote
- Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.329-338.
-
Sparck Jones, K.: ¬A statistical interpretation of term specifity and its application in retrieval (1972)
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-
Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986)
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- Source
- Information processing and management. 22(1986) no.6, S.465-476
-
Tsai, C.-F.; Hu, Y.-H.; Chen, Z.-Y.: Factors affecting rocchio-based pseudorelevance feedback in image retrieval (2015)
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- Abstract
- Pseudorelevance feedback (PRF) was proposed to solve the limitation of relevance feedback (RF), which is based on the user-in-the-loop process. In PRF, the top-k retrieved images are regarded as PRF. Although the PRF set contains noise, PRF has proven effective for automatically improving the overall retrieval result. To implement PRF, the Rocchio algorithm has been considered as a reasonable and well-established baseline. However, the performance of Rocchio-based PRF is subject to various representation choices (or factors). In this article, we examine these factors that affect the performance of Rocchio-based PRF, including image-feature representation, the number of top-ranked images, the weighting parameters of Rocchio, and similarity measure. We offer practical insights on how to optimize the performance of Rocchio-based PRF by choosing appropriate representation choices. Our extensive experiments on NUS-WIDE-LITE and Caltech 101 + Corel 5000 data sets show that the optimal feature representation is color moment + wavelet texture in terms of retrieval efficiency and effectiveness. Other representation choices are that using top-20 ranked images as pseudopositive and pseudonegative feedback sets with the equal weight (i.e., 0.5) by the correlation and cosine distance functions can produce the optimal retrieval result.
-
Bhansali, D.; Desai, H.; Deulkar, K.: ¬A study of different ranking approaches for semantic search (2015)
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-
Yu, K.; Tresp, V.; Yu, S.: ¬A nonparametric hierarchical Bayesian framework for information filtering (2004)
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- Source
- SIGIR'04: Proceedings of the 27th Annual International ACM-SIGIR Conference an Research and Development in Information Retrieval. Ed.: K. Järvelin, u.a
-
Niemi, T.; Junkkari, M.; Järvelin, K.; Viita, S.: Advanced query language for manipulating complex entities (2004)
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-
Jones, K.: Linguistic searching versus relevance ranking : DR-LINK and TARGET (1999)
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-
Khoo, C.S.G.; Wan, K.-W.: ¬A simple relevancy-ranking strategy for an interface to Boolean OPACs (2004)
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- Source
- Electronic library. 22(2004) no.2, S.112-120
-
Smeaton, A.F.; Rijsbergen, C.J. van: ¬The retrieval effects of query expansion on a feedback document retrieval system (1983)
0.01
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- Date
- 30. 3.2001 13:32:22
-
Back, J.: ¬An evaluation of relevancy ranking techniques used by Internet search engines (2000)
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- Date
- 25. 8.2005 17:42:22
-
Clarke, C.L.A.; Cormack, G.V.; Burkowski, F.J.: Shortest substring ranking : multitext experiments for TREC-4 (1996)
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- Source
- The Fourth Text Retrieval Conference (TREC-4). Ed.: K. Harman
-
Savoy, J.; Ndarugendamwo, M.; Vrajitoru, D.: Report on the TREC-4 experiment : combining probabilistic and vector-space schemes (1996)
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- Source
- The Fourth Text Retrieval Conference (TREC-4). Ed.: K. Harman
-
Belkin, N.J.; Cool, C.; Koenemann, J.; Ng, K.B.; Park, S.: Using relevance feedback and ranking in interactive searching (1996)
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- Source
- The Fourth Text Retrieval Conference (TREC-4). Ed.: K. Harman
