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Journal articles on the topic 'Contextual search'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Abdolmaleki, Abbas, David Simões, Nuno Lau, Luís Paulo Reis, and Gerhard Neumann. "Contextual Direct Policy Search." Journal of Intelligent & Robotic Systems 96, no. 2 (2019): 141–57. http://dx.doi.org/10.1007/s10846-018-0968-4.

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2

Kiran, Rathi, and Mitula Pandya. "Contextual Search Results Clustering Using Lingo with Synonymity." International Journal of Scientific Research 2, no. 6 (2012): 183–85. http://dx.doi.org/10.15373/22778179/june2013/58.

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3

Leme, Renato Paes, and Jon Schneider. "Contextual Search via Intrinsic Volumes." SIAM Journal on Computing 51, no. 4 (2022): 1096–125. http://dx.doi.org/10.1137/20m1385718.

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4

Melucci, Massimo. "Contextual Search: A Computational Framework." Foundations and Trends® in Information Retrieval 6, no. 4-5 (2012): 257–405. http://dx.doi.org/10.1561/1500000023.

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5

Toh, Yi Ni, Caitlin A. Sisk, and Yuhong V. Jiang. "Contextual cueing in preview search." Attention, Perception, & Psychophysics 82, no. 6 (2020): 2862–75. http://dx.doi.org/10.3758/s13414-020-02052-9.

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6

Hodsoll, John P., and Glyn W. Humphreys. "Preview Search and Contextual Cuing." Journal of Experimental Psychology: Human Perception and Performance 31, no. 6 (2005): 1346–58. http://dx.doi.org/10.1037/0096-1523.31.6.1346.

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7

Toh, Yi Ni, Caitlin A. Sisk, and Yuhong V. Jiang. "Contextual cueing in preview search." Journal of Vision 20, no. 11 (2020): 1259. http://dx.doi.org/10.1167/jov.20.11.1259.

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8

Hoffmann, Joachim, and Albrecht Sebald. "Local Contextual Cuing in Visual Search." Experimental Psychology 52, no. 1 (2005): 31–38. http://dx.doi.org/10.1027/1618-3169.52.1.31.

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Abstract. Previous research has indicated that covariations between the global layout of search displays and target locations result in contextual cuing: the global context guides attention to probable target locations. The present experiments extend these findings by showing that local redundancies also facilitate visual search. Participants searched for randomly located targets in invariant homogenous displays, i.e., the global context provided information neither about the location nor about the identity of the target. The only redundancy referred to spatial relations between the targets an

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9

Walenchok, S., M. Hout, and S. Goldinger. "Categorical Contextual Cueing in Visual Search." Journal of Vision 14, no. 10 (2014): 1074. http://dx.doi.org/10.1167/14.10.1074.

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10

Sebyakin, Alexey. "The Possibilities of Using Contextual Information Search on Computer Media to Identify, Investigate and Prevent Crimes." Russian Journal of Criminology 13, no. 2 (2019): 262–70. http://dx.doi.org/10.17150/2500-4255.2019.13(2).262-270.

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Contextual search is now actively used by various divisions of the law enforcement bodies to identify, investigate and prevent crimes. This instrument could be used both to search information online and to analyze local digital information carriers. Still, the task of contextual search does not currently have a single definition, and its place among the tasks of computer forensic examination is not clearly determined. The author examines various problem aspects of contextual search: its concept, its place among the tasks of forensic examination, modern understanding of the task of contextual s

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11

Painter, Michael, Bruno Lacerda, and Nick Hawes. "Convex Hull Monte-Carlo Tree-Search." Proceedings of the International Conference on Automated Planning and Scheduling 30 (June 1, 2020): 217–25. http://dx.doi.org/10.1609/icaps.v30i1.6664.

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This work investigates Monte-Carlo planning for agents in stochastic environments, with multiple objectives. We propose the Convex Hull Monte-Carlo Tree-Search (CHMCTS) framework, which builds upon Trial Based Heuristic Tree Search and Convex Hull Value Iteration (CHVI), as a solution to multi-objective planning in large environments. Moreover, we consider how to pose the problem of approximating multi-objective planning solutions as a contextual multi-armed bandits problem, giving a principled motivation for how to select actions from the view of contextual regret. This leads us to the use of

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12

Zhen Liu, Houqiang Li, Wengang Zhou, Ruizhen Zhao, and Qi Tian. "Contextual Hashing for Large-Scale Image Search." IEEE Transactions on Image Processing 23, no. 4 (2014): 1606–14. http://dx.doi.org/10.1109/tip.2014.2305072.

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13

Lobel, Ilan, Renato Paes Leme, and Adrian Vladu. "Multidimensional Binary Search for Contextual Decision-Making." Operations Research 66, no. 5 (2018): 1346–61. http://dx.doi.org/10.1287/opre.2018.1722.

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14

Calisi, D., L. Iocchi, D. Nardi, G. Randelli, and V. A. Ziparo. "Improving Search and Rescue Using Contextual Information." Advanced Robotics 23, no. 9 (2009): 1199–216. http://dx.doi.org/10.1163/156855309x452539.

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15

Yang, Xi, Xinbo Gao, Bin Song, and Dong Yang. "Aurora image search with contextual CNN feature." Neurocomputing 281 (March 2018): 67–77. http://dx.doi.org/10.1016/j.neucom.2017.11.059.

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16

Cha, Guang-Ho. "Capturing contextual relationship for effective media search." Multimedia Tools and Applications 56, no. 2 (2010): 351–64. http://dx.doi.org/10.1007/s11042-010-0670-4.

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17

Zeng, Anxiang, Han Yu, Qing Da, et al. "Improving Search Engine Efficiency through Contextual Factor Selection." AI Magazine 42, no. 2 (2021): 50–58. http://dx.doi.org/10.1609/aimag.v42i2.15099.

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Learning to rank (LTR) is an important artificial intelligence (AI) approach supporting the operation of many search engines. In large-scale search systems, the ranking results are continually improved with the introduction of more factors to be considered by LTR. However, the more factors being considered, the more computation resources required, which in turn, results in increased system response latency. Therefore, removing redundant factors can significantly improve search engine efficiency. In this paper, we report on our experience incorporating our Contextual Factor Selection (CFS) deep

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18

Nabeta, Tomohiro, Fuminori Ono, and Jun-Ichiro Kawahara. "Transfer of Spatial Context from Visual to Haptic Search." Perception 32, no. 11 (2003): 1351–58. http://dx.doi.org/10.1068/p5135.

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Under incidental learning conditions, spatial layouts can be acquired implicitly and facilitate visual search (contextual-cueing effect). We examined whether the contextual-cueing effect is specific to the visual modality or transfers to the haptic modality. The participants performed 320 (experiment 1) or 192 (experiment 2) visual search trials based on a typical contextual-cueing paradigm, followed by haptic search trials in which half of the trials had layouts used in the previous visual search trials. The visual contextual-cueing effect was obtained in the learning phase. More importantly,

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19

Kole, J. A., A. F. Healy, D. M. Fierman, and L. E. Bourne. "Contextual memory and skill transfer in category search." Memory & Cognition 38, no. 1 (2009): 67–82. http://dx.doi.org/10.3758/mc.38.1.67.

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20

Esser-Adomako, Elizabeth G., Patrick Mead, Shane Kelly, and Matthew S. Peterson. "Contextual Relearning Following Target Relocation in Visual Search." Journal of Vision 19, no. 10 (2019): 200b. http://dx.doi.org/10.1167/19.10.200b.

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21

Aivar, M. Pilar, Sandra Miguel, and Elena Sanz. "Contextual Cueing in a Comparative Visual Search task." Journal of Vision 19, no. 10 (2019): 308b. http://dx.doi.org/10.1167/19.10.308b.

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22

Jegou, H., C. Schmid, H. Harzallah, and J. Verbeek. "Accurate Image Search Using the Contextual Dissimilarity Measure." IEEE Transactions on Pattern Analysis and Machine Intelligence 32, no. 1 (2010): 2–11. http://dx.doi.org/10.1109/tpami.2008.285.

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23

Beesley, Tom, Miguel A. Vadillo, Daniel Pearson, and David R. Shanks. "Configural learning in contextual cuing of visual search." Journal of Experimental Psychology: Human Perception and Performance 42, no. 8 (2016): 1173–85. http://dx.doi.org/10.1037/xhp0000185.

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24

Neider, M. B., and G. J. Zelinsky. "Effects of scene-based contextual guidance on search." Journal of Vision 5, no. 8 (2010): 414. http://dx.doi.org/10.1167/5.8.414.

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25

Zheng, Liang, Shengjin Wang, Jingdong Wang, and Qi Tian. "Accurate Image Search with Multi-Scale Contextual Evidences." International Journal of Computer Vision 120, no. 1 (2016): 1–13. http://dx.doi.org/10.1007/s11263-016-0889-2.

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26

Zang, Xuelian, Lingyun Huang, Xiuna Zhu, Hermann J. Müller, and Zhuanghua Shi. "Influences of luminance contrast and ambient lighting on visual context learning and retrieval." Attention, Perception, & Psychophysics 82, no. 8 (2020): 4007–24. http://dx.doi.org/10.3758/s13414-020-02106-y.

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Abstract Invariant spatial context can guide attention and facilitate visual search, an effect referred to as “contextual cueing.” Most previous studies on contextual cueing were conducted under conditions of photopic vision and high search item to background luminance contrast, leaving open the question whether the learning and/or retrieval of context cues depends on luminance contrast and ambient lighting. Given this, we conducted three experiments (each contains two subexperiments) to compare contextual cueing under different combinations of luminance contrast (high/low) and ambient lightin

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27

Manginelli, Angela A., Franziska Geringswald, and Stefan Pollmann. "Visual Search Facilitation in Repeated Displays Depends on Visuospatial Working Memory." Experimental Psychology 59, no. 1 (2012): 47–54. http://dx.doi.org/10.1027/1618-3169/a000125.

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When distractor configurations are repeated over time, visual search becomes more efficient, even if participants are unaware of the repetition. This contextual cueing is a form of incidental, implicit learning. One might therefore expect that contextual cueing does not (or only minimally) rely on working memory resources. This, however, is debated in the literature. We investigated contextual cueing under either a visuospatial or a nonspatial (color) visual working memory load. We found that contextual cueing was disrupted by the concurrent visuospatial, but not by the color working memory lo

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28

Marek, Nico, and Stefan Pollmann. "Contextual-Cueing beyond the Initial Field of View—A Virtual Reality Experiment." Brain Sciences 10, no. 7 (2020): 446. http://dx.doi.org/10.3390/brainsci10070446.

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In visual search, participants can incidentally learn spatial target-distractor configurations, leading to shorter search times for repeated compared to novel configurations. Usually, this is tested within the limited visual field provided by a computer monitor. While contextual cueing is typically investigated on two-dimensional screens, we present for the first time an implementation of a classic contextual cueing task (search for a T-shape among L-shapes) in a three-dimensional virtual environment. This enabled us to test if the typical finding of incidental learning of repeated search conf

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29

Pollmann, Stefan, Lisa Rosenblum, Stefanie Linnhoff, et al. "Preserved Contextual Cueing in Realistic Scenes in Patients with Age-Related Macular Degeneration." Brain Sciences 10, no. 12 (2020): 941. http://dx.doi.org/10.3390/brainsci10120941.

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Foveal vision loss has been shown to reduce efficient visual search guidance due to contextual cueing by incidentally learned contexts. However, previous studies used artificial (T- among L-shape) search paradigms that prevent the memorization of a target in a semantically meaningful scene. Here, we investigated contextual cueing in real-life scenes that allow explicit memory of target locations in semantically rich scenes. In contrast to the contextual cueing deficits in artificial scenes, contextual cueing in patients with age-related macular degeneration (AMD) did not differ from age-matche

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30

Beesley, Tom, Miguel A. Vadillo, Daniel Pearson, and David R. Shanks. "Pre-exposure of repeated search configurations facilitates subsequent contextual cuing of visual search." Journal of Experimental Psychology: Learning, Memory, and Cognition 41, no. 2 (2015): 348–62. http://dx.doi.org/10.1037/xlm0000033.

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31

Ivanechko, N., V. Dudar, and V. Pavelko. "Search engine marketing." Galic'kij ekonomičnij visnik 74, no. 1 (2022): 158–62. http://dx.doi.org/10.33108/galicianvisnyk_tntu2022.01.158.

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The article considers the elements of Internet marketing. It is determined that search marketing is a search engine optimization and a modern method of site promotion by optimizing the resource for social networks. The classification of search queries is carried out according to the following features: query competitiveness, query frequency, query value, query geodependence, query regionality, query seasonality, query time of day. It is established that the sign of competitiveness of the request is divided into highly competitive, medium competitive, low competitive; the query frequency attrib

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32

Ivanechko, N., V. Dudar, and V. Pavelko. "Search engine marketing." Galic'kij ekonomičnij visnik 74, no. 1 (2022): 158–62. http://dx.doi.org/10.33108/galicianvisnyk_tntu2022.01.158.

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The article considers the elements of Internet marketing. It is determined that search marketing is a search engine optimization and a modern method of site promotion by optimizing the resource for social networks. The classification of search queries is carried out according to the following features: query competitiveness, query frequency, query value, query geodependence, query regionality, query seasonality, query time of day. It is established that the sign of competitiveness of the request is divided into highly competitive, medium competitive, low competitive; the query frequency attrib

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33

Kumar, Pilli Suresh. "Core Technologies in Semantic Search Engines." International Journal of Research and Innovation in Applied Science X, no. IV (2025): 287–97. https://doi.org/10.51584/ijrias.2025.10040023.

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Semantic search engines have revolutionized the way we retrieve information from the web by focusing on user intent and contextual meaning, rather than relying solely on keyword matching. This is enabled by core technologies like NLP, Knowledge Graphs, AI, ML. NLP helps search engines make sense of human language, allowing them to understand how words and phrases relate to each other. This utilizes Knowledge Graphs to improve search results, as this builds the data into relations, supplying the search engine with the ability to return more precise and contextual results. AI and ML algorithms w

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34

Li, Yi, Zhihui Yuan, Yujie Li, and Jing Liu. "Factors influencing search engine usage behavior." Social Behavior and Personality: an international journal 46, no. 1 (2018): 1–10. http://dx.doi.org/10.2224/sbp.6211.

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We analyzed the effect of individual factors, contextual factors, and perception of search engine advertising on users' search engine usage behavior. The sample comprised 404 Chinese who used search engines in the context of their paid employment. Results showed that (a) perceived search skills and perceived search engine reliance significantly and positively impacted users' general search engine usage, (b) perceived advertising clutter reduced the beneficial effects of perceived search skills on users' general search engine usage, (c) users with higher perceived search engine reliance preferr

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35

Yokoi, Keisuke, Minh-Quoc Nghiem, Yuichiroh Matsubayashi, and Akiko Aizawa. "Contextual Analysis of Mathematical Expressions for Advanced Mathematical Search." Polibits 43 (June 30, 2011): 81–86. http://dx.doi.org/10.17562/pb-43-11.

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36

Sayim, B., G. Westheimer, and M. H. Herzog. "Quantifying target conspicuity in contextual modulation by visual search." Journal of Vision 11, no. 1 (2011): 6. http://dx.doi.org/10.1167/11.1.6.

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37

Conci, M., and A. von Muhlenen. "Figure-ground segmentation determines contextual learning in visual search." Journal of Vision 9, no. 8 (2010): 926. http://dx.doi.org/10.1167/9.8.926.

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38

Ogawa, H., and K. Watanabe. "The time course of contextual modulation in visual search." Journal of Vision 6, no. 6 (2010): 840. http://dx.doi.org/10.1167/6.6.840.

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39

Beesley, T., and David R. Shanks. "Investigating cue competition in contextual cuing of visual search." Journal of Experimental Psychology: Learning, Memory, and Cognition 38, no. 3 (2012): 709–25. http://dx.doi.org/10.1037/a0024885.

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40

Manginelli, Angela A., and Stefan Pollmann. "Misleading contextual cues: How do they affect visual search?" Psychological Research 73, no. 2 (2008): 212–21. http://dx.doi.org/10.1007/s00426-008-0211-1.

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41

Brézillon, Patrick, Laure Léger, and Charles Tijus. "Modeling user search on a website by contextual graphs." Revue d'intelligence artificielle 23, no. 4 (2009): 467–84. http://dx.doi.org/10.3166/ria.23.467-484.

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42

Neider, Mark B., and Arthur F. Kramer. "Older Adults Capitalize on Contextual Information to Guide Search." Experimental Aging Research 37, no. 5 (2011): 539–71. http://dx.doi.org/10.1080/0361073x.2011.619864.

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43

Brockmole, James R., and John M. Henderson. "Using real-world scenes as contextual cues for search." Visual Cognition 13, no. 1 (2006): 99–108. http://dx.doi.org/10.1080/13506280500165188.

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44

Conci, Markus, and Hermann J. Müller. "Contextual learning of multiple target locations in visual search." Visual Cognition 20, no. 7 (2012): 746–70. http://dx.doi.org/10.1080/13506285.2012.694376.

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45

Barry, Wail A., and Abdu Mohamed Dawood Hafiz. "Contemporary quantitative and qualitative public relations research methods in Arab scholarly studies: A meta-research." SEARCH Journal Media and Communication Research 16, no. 1 (2024): 1–15. http://dx.doi.org/10.58946/search-16.1.p1.

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Communication scholars in the Arab countries have started to address new fields of scholarly literature in the public relations discipline over the last decade. The crucial change induced by the Arab Spring in many Arab countries has propelled public relations scholars to investigate the type of changes that affect scholarly research methods in the genre of communication at large, and public relations in particular. In the current study, the meta-analysis method was utilised to examine a total of 61 scholarly work (2012 to 2022), from seven Arab countries (Egypt, Iraq, Kuwait, United Arab Emir

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46

Cheuk, Ka Po, Saša Baškarada, and Andy Koronios. "Contextual factors in knowledge reuse." VINE Journal of Information and Knowledge Management Systems 47, no. 2 (2017): 194–210. http://dx.doi.org/10.1108/vjikms-10-2016-0056.

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Purpose This paper aims to answer calls for more research on how contextual factors influence the effectiveness of knowledge reuse. Design/methodology/approach The findings are based on an in-depth case study conducted in the sales department of a large white goods company in Australia. Findings Six contextual factors identified include purpose, time pressure, language, accessibility, author and date. A mismatch in purpose between knowledge creation and reuse is most likely to reduce knowledge reuse effectiveness. Time pressures may lead to an increase in errors associated with search question

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47

Zhang, Xiaojuan, Shuguang Han, and Wei Lu. "Automatic prediction of news intent for search queries." Electronic Library 36, no. 5 (2018): 938–58. http://dx.doi.org/10.1108/el-06-2017-0134.

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Purpose The purpose of this paper is to predict news intent by exploring contextual and temporal features directly mined from a general search engine query log. Design/methodology/approach First, a ground-truth data set with correctly marked news and non-news queries was built. Second, a detailed analysis of the search goals and topics distribution of news/non-news queries was conducted. Third, three news features, that is, the relationship between entity and contextual words extended from query sessions, topical similarity among clicked results and temporal burst point were obtained. Finally,

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48

Van der Elst, Michael, Birgitte Schoenmakers, Eva Dierckx, et al. "A search for relevant contextual factors in intervention studies: a stepwise approach with online information." BMJ Open 12, no. 9 (2022): e057048. http://dx.doi.org/10.1136/bmjopen-2021-057048.

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ObjectiveThe aim of the present study is to describe a stepwise approach to study which contextual factors might moderate the effect of healthcare interventions and to test feasibility of this approach within the D-SCOPE project.DesignExploratory case study.SettingIn the D-SCOPE project, a complex intervention by means of home visits was set up to improve access to tailored care in three municipalities (Ghent, Knokke-Heist and Tienen).MethodsOne designed and tested an approach including five steps: (1) a theoretical/conceptual discussion of relevant contextual factor domains was held; (2) a se

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49

Atsidakou, Alexia, Constantine Caramanis, Evangelia Gergatsouli, Orestis Papadigenopoulos, and Christos Tzamos. "Contextual Pandora’s Box." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 10 (2024): 10944–52. http://dx.doi.org/10.1609/aaai.v38i10.28969.

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Pandora’s Box is a fundamental stochastic optimization problem, where the decision-maker must find a good alternative, while minimizing the search cost of exploring the value of each alternative. In the original formulation, it is assumed that accurate distributions are given for the values of all the alternatives, while recent work studies the online variant of Pandora’s Box where the distributions are originally unknown. In this work, we study Pandora’s Box in the online setting, while incorporating context. At each round, we are presented with a number of alternatives each having a context,

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50

Serhiy, Mashkovskyi. "DEVELOPING OF THE RELATED DATA SEARCH LSA-BASED ALGORITHM AND ITS PROGRAMMED REALIZATION." EUREKA: Physics and Engineering, no. 1 (January 31, 2018): 47–56. https://doi.org/10.21303/2461-4262.2018.00468.

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In this article let’s consider the theoretical basis of the data search in large data ordered arrays based on the context of the search request and tracking of semantic relationships. Also the first steps towards the practical implementation of this task are proposed. Simple program to check author’s thoughts has been developed. All the researches have been made with the VK social network. Internal API VK was used as retrieving data tool. The final results say that the VK’s content has many opportunities to make them more useful and searchable, which means that it is possible

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