-
Bauckhage, C.: Marginalizing over the PageRank damping factor (2014)
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Bornmann, L.; Mutz, R.: From P100 to P100' : a new citation-rank approach (2014)
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- Date
- 22. 8.2014 17:05:18
-
Tober, M.; Hennig, L.; Furch, D.: SEO Ranking-Faktoren und Rang-Korrelationen 2014 : Google Deutschland (2014)
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- Date
- 13. 9.2014 14:45:22
-
Behnert, C.; Borst, T.: Neue Formen der Relevanz-Sortierung in bibliothekarischen Informationssystemen : das DFG-Projekt LibRank (2015)
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Ravana, S.D.; Rajagopal, P.; Balakrishnan, V.: Ranking retrieval systems using pseudo relevance judgments (2015)
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- Date
- 20. 1.2015 18:30:22
18. 9.2018 18:22:56
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Behnert, C.; Plassmeier, K.; Borst, T.; Lewandowski, D.: Evaluierung von Rankingverfahren für bibliothekarische Informationssysteme (2019)
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Cecchini, R.L.; Lorenzetti, C.M.; Maguitman, A.G.; Brignole, N.B.: Multiobjective evolutionary algorithms for context-based search (2010)
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- Abstract
- Formulating high-quality queries is a key aspect of context-based search. However, determining the effectiveness of a query is challenging because multiple objectives, such as high precision and high recall, are usually involved. In this work, we study techniques that can be applied to evolve contextualized queries when the criteria for determining query quality are based on multiple objectives. We report on the results of three different strategies for evolving queries: (a) single-objective, (b) multiobjective with Pareto-based ranking, and (c) multiobjective with aggregative ranking. After a comprehensive evaluation with a large set of topics, we discuss the limitations of the single-objective approach and observe that both the Pareto-based and aggregative strategies are highly effective for evolving topical queries. In particular, our experiments lead us to conclude that the multiobjective techniques are superior to a baseline as well as to well-known and ad hoc query reformulation techniques.
-
Biskri, I.; Rompré, L.: Using association rules for query reformulation (2012)
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- Source
- Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a
-
Habernal, I.; Konopík, M.; Rohlík, O.: Question answering (2012)
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- Source
- Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a
-
Van der Veer Martens, B.; Fleet, C. van: Opening the black box of "relevance work" : a domain analysis (2012)
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-
Baloh, P.; Desouza, K.C.; Hackney, R.: Contextualizing organizational interventions of knowledge management systems : a design science perspectiveA domain analysis (2012)
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- Date
- 11. 6.2012 14:22:34
-
Soulier, L.; Jabeur, L.B.; Tamine, L.; Bahsoun, W.: On ranking relevant entities in heterogeneous networks using a language-based model (2013)
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- Date
- 22. 3.2013 19:34:49
-
Lee, J.-T.; Seo, J.; Jeon, J.; Rim, H.-C.: Sentence-based relevance flow analysis for high accuracy retrieval (2011)
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-
Nunes, S.; Ribeiro, C.; David, G.: Term weighting based on document revision history (2011)
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-
Liu, R.-L.; Huang, Y.-C.: Ranker enhancement for proximity-based ranking of biomedical texts (2011)
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-
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)
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-
Tsai, C.-F.; Hu, Y.-H.; Chen, Z.-Y.: Factors affecting rocchio-based pseudorelevance feedback in image retrieval (2015)
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Lee, J.; Min, J.-K.; Oh, A.; Chung, C.-W.: Effective ranking and search techniques for Web resources considering semantic relationships (2014)
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Zhu, J.; Han, L.; Gou, Z.; Yuan, X.: ¬A fuzzy clustering-based denoising model for evaluating uncertainty in collaborative filtering recommender systems (2018)
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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.
-
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)
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