Search (77 results, page 3 of 4)

  • × theme_ss:"Retrievalalgorithmen"
  1. Van der Veer Martens, B.; Fleet, C. van: Opening the black box of "relevance work" : a domain analysis (2012) 0.01 
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  2. Liu, J.; Liu, C.: Personalization in text information retrieval : a survey (2020) 0.01 
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  3. Carpineto, C.; Romano, G.: Order-theoretical ranking (2000) 0.01 
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  4. Lopez-Pujalte, C.; Guerrero Bote, V.P.; Moya-Anegón, F. de: Evaluation of the application of genetic algorithms to relevance feedback (2003) 0.01 
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  5. Watters, C.; Amoudi, A.: Geosearcher : location-based ranking of search engine results (2003) 0.01 
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  6. López-Pujalte, C.; Guerrero-Bote, V.P.; Moya-Anegón, F. de: Order-based fitness functions for genetic algorithms applied to relevance feedback (2003) 0.01 
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  7. Dannenberg, R.B.; Birmingham, W.P.; Pardo, B.; Hu, N.; Meek, C.; Tzanetakis, G.: ¬A comparative evaluation of search techniques for query-by-humming using the MUSART testbed (2007) 0.01 
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  8. Schaefer, A.; Jordan, M.; Klas, C.-P.; Fuhr, N.: Active support for query formulation in virtual digital libraries : a case study with DAFFODIL (2005) 0.01 
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  9. Lee, J.-T.; Seo, J.; Jeon, J.; Rim, H.-C.: Sentence-based relevance flow analysis for high accuracy retrieval (2011) 0.01 
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  10. Nunes, S.; Ribeiro, C.; David, G.: Term weighting based on document revision history (2011) 0.01 
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  11. Liu, R.-L.; Huang, Y.-C.: Ranker enhancement for proximity-based ranking of biomedical texts (2011) 0.01 
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  12. Costa Carvalho, A. da; Rossi, C.; Moura, E.S. de; Silva, A.S. da; Fernandes, D.: LePrEF: Learn to precompute evidence fusion for efficient query evaluation (2012) 0.01 
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  13. Tsai, C.-F.; Hu, Y.-H.; Chen, Z.-Y.: Factors affecting rocchio-based pseudorelevance feedback in image retrieval (2015) 0.01 
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  14. Lee, J.; Min, J.-K.; Oh, A.; Chung, C.-W.: Effective ranking and search techniques for Web resources considering semantic relationships (2014) 0.01 
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  15. Zhu, J.; Han, L.; Gou, Z.; Yuan, X.: ¬A fuzzy clustering-based denoising model for evaluating uncertainty in collaborative filtering recommender systems (2018) 0.01 
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    Abstract
    Recommender systems are effective in predicting the most suitable products for users, such as movies and books. To facilitate personalized recommendations, the quality of item ratings should be guaranteed. However, a few ratings might not be accurate enough due to the uncertainty of user behavior and are referred to as natural noise. In this article, we present a novel fuzzy clustering-based method for detecting noisy ratings. The entropy of a subset of the original ratings dataset is used to indicate the data-driven uncertainty, and evaluation metrics are adopted to represent the prediction-driven uncertainty. After the repetition of resampling and the execution of a recommendation algorithm, the entropy and evaluation metrics vectors are obtained and are empirically categorized to identify the proportion of the potential noise. Then, the fuzzy C-means-based denoising (FCMD) algorithm is performed to verify the natural noise under the assumption that natural noise is primarily the result of the exceptional behavior of users. Finally, a case study is performed using two real-world datasets. The experimental results show that our proposal outperforms previous proposals and has an advantage in dealing with natural noise.
  16. González-Ibáñez, R.; Esparza-Villamán, A.; Vargas-Godoy, J.C.; Shah, C.: ¬A comparison of unimodal and multimodal models for implicit detection of relevance in interactive IR (2019) 0.01 
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  17. Abu-Salem, H.; Al-Omari, M.; Evens, M.W.: Stemming methodologies over individual query words for an Arabic information retrieval system (1999) 0.01 
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  18. MacFarlane, A.; Robertson, S.E.; McCann, J.A.: Parallel computing for passage retrieval (2004) 0.01 
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    Date
    20. 1.2007 18:30:22
  19. Losada, D.E.; Barreiro, A.: Emebedding term similarity and inverse document frequency into a logical model of information retrieval (2003) 0.01 
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    Date
    22. 3.2003 19:27:23
  20. Bornmann, L.; Mutz, R.: From P100 to P100' : a new citation-rank approach (2014) 0.01 
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    Date
    22. 8.2014 17:05:18

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