Relevant bibliographies by topics / CONTEXT-AWARE RECOMMENDER

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'CONTEXT-AWARE RECOMMENDER'

Author: Grafiati
Published: 11 September 2023
Last updated: 25 July 2025

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Journal articles on the topic "CONTEXT-AWARE RECOMMENDER"

1

Adomavicius, Gediminas, Bamshad Mobasher, Francesco Ricci, and Alexander Tuzhilin. "Context-Aware Recommender Systems." AI Magazine 32, no. 3 (2011): 67. http://dx.doi.org/10.1609/aimag.v32i3.2364.

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Context-aware recommender systems (CARS) generate more relevant recommendations by adapting them to the specific contextual situation of the user. This article explores how contextual information can be used to create more intelligent and useful recommender systems. It provides an overview of the multifaceted notion of context, discusses several approaches for incorporating contextual information in recommendation process, and illustrates the usage of such approaches in several application areas where different types of contexts are exploited. The article concludes by discussing the challenges
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WANG, Li-Cai, Xiang-Wu MENG, and Yu-Jie ZHANG. "Context-Aware Recommender Systems." Journal of Software 23, no. 1 (2012): 1–20. http://dx.doi.org/10.3724/sp.j.1001.2012.04100.

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Verbert, Katrien, Erik Duval, Stefanie Lindstaedt, and Denis Gillet. "Context-aware Recommender Systems." JUCS - Journal of Universal Computer Science 16, no. (16) (2010): 2175–78. https://doi.org/10.3217/jucs-016-16.

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Azri, Muhammad Haziq Fikri Bin, Su-Cheng Haw, Kok-Why Ng, and Mohamad Firdaus Mat Saad. "Context-Aware Job Recommender System." JOIV : International Journal on Informatics Visualization 9, no. 2 (2025): 877. https://doi.org/10.62527/joiv.9.2.3021.

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Context-aware recommendation systems have emerged as essential to interactive web content and online job search. Primarily, since so many job offers are published on different online platforms, it can make the users take some time to find good opportunities that match exactly what they are looking for, as well as countless qualified candidates and other characteristics within that context, such as temporality. This comes as no surprise, as many practitioners and researchers have resorted to machine learning to create context-aware job recommendation systems that cater not only to job seekers.
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Pisotskyi, Marian, and Alexey Botchkaryov. "Online Video Platform with Context-aware Content-based Recommender System." Advances in Cyber-Physical Systems 6, no. 1 (2021): 46–53. http://dx.doi.org/10.23939/acps2021.01.046.

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The problem of developing an online video platform with a context-aware content-based recommender system has been considered. Approaches to developing online video platforms have been considered. A comparison of popular online video platforms has been presented. A method of context-aware content-based recommendation of videos has been proposed. A method involves saving information about user interaction with video, obtaining and storing information about which videos the user liked, determining user context, composing a profile of user preferences, composing a profile of user preferences depen
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Kavu, Tatenda D., Kudakwashe Dube, and Peter G. Raeth. "Holistic User Context-Aware Recommender Algorithm." Mathematical Problems in Engineering 2019 (September 29, 2019): 1–15. http://dx.doi.org/10.1155/2019/3965845.

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Existing recommender algorithms lack dynamism, human focus, and serendipitous recommendations. The literature indicates that the context of a user influences user decisions, and when incorporated in recommender systems (RSs), novel and serendipitous recommendations can be realized. This article shows that social, cultural, psychological, and economic contexts of a user influence user traits or decisions. The article demonstrates a novel approach of incorporating holistic user context-aware knowledge in an algorithm to solve the highlighted problems. Web content mining and collaborative filteri
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Qassimi, Sara, El Hassan Abdelwahed, and Meriem Hafidi. "Folksonomy Graphs Based Context-Aware Recommender System Using Spectral Clustering." International Journal of Machine Learning and Computing 10, no. 1 (2020): 63–68. http://dx.doi.org/10.18178/ijmlc.2020.10.1.899.

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Iqbal, Misbah, Mustansar Ali Ghazanfar, Asma Sattar, et al. "Kernel Context Recommender System (KCR): A Scalable Context-Aware Recommender System Algorithm." IEEE Access 7 (2019): 24719–37. http://dx.doi.org/10.1109/access.2019.2897003.

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Ali, Waqar, Jay Kumar, Cobbinah Bernard Mawuli, Lei She, and Jie Shao. "Dynamic context management in context-aware recommender systems." Computers and Electrical Engineering 107 (April 2023): 108622. http://dx.doi.org/10.1016/j.compeleceng.2023.108622.

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Naha, Sanchita, and Sudeep Marwaha. "Context-Aware Recommender System for Maize Cultivation." Journal of Community Mobilization and Sustainable Development 15, no. 2 (2020): 485–90. http://dx.doi.org/10.5958/2231-6736.2020.00034.

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Dissertations / Theses on the topic "CONTEXT-AWARE RECOMMENDER"

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Liu, Xiaohu. "Context-aware recommender systems for implicit data." Thesis, University of York, 2014. http://etheses.whiterose.ac.uk/13237/.

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Recommender systems are software tools and techniques providing suggestions and recommendations for items to be of use to a user. These sug- gestions can help users make better decisions on choosing products or services, such as which film to watch, what music to listen to or which travel insurance to buy. When making suggestions, many recommender systems do not consider contextual information, such as location or time [5]. Recommender systems that make use of contextual information are called context-aware recommender systems. Many context-aware recommender systems can not generate reliable r
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Al-Ghossein, Marie. "Context-aware recommender systems for real-world applications." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLT008/document.

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Les systèmes de recommandation se sont révélés être des outils efficaces pour aider les utilisateurs à faire face à la surcharge informationnelle. D’importants progrès ont été réalisés dans le domaine durant les deux dernières décennies, menant en particulier à l’exploitation de l’information contextuelle pour modéliser l’aspect dynamique des utilisateurs et des articles. La définition traditionnelle du contexte, adoptée dans la plupart des systèmes de recommandation contextuels, ne répond pas à plusieurs contraintes rencontrées dans les applications du monde réel. Dans cette thèse, nous abord
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Al-Ghossein, Marie. "Context-aware recommender systems for real-world applications." Electronic Thesis or Diss., Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLT008.

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Les systèmes de recommandation se sont révélés être des outils efficaces pour aider les utilisateurs à faire face à la surcharge informationnelle. D’importants progrès ont été réalisés dans le domaine durant les deux dernières décennies, menant en particulier à l’exploitation de l’information contextuelle pour modéliser l’aspect dynamique des utilisateurs et des articles. La définition traditionnelle du contexte, adoptée dans la plupart des systèmes de recommandation contextuels, ne répond pas à plusieurs contraintes rencontrées dans les applications du monde réel. Dans cette thèse, nous abord
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Li, Siying. "Context-aware recommender system for system of information systems." Thesis, Compiègne, 2021. http://www.theses.fr/2021COMP2602.

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Travailler en collaboration n’est plus une question mais une réalité, la question qui se pose aujourd’hui concerne la mise en œuvre de la collaboration de façon à ce qu’elle soit la plus réussie possible. Cependant, une collaboration réussie n’est pas facile et est conditionnée par différents facteurs qui peuvent l’influencer. Il est donc nécessaire de considérer ces facteurs au sein du contexte de collaboration pour favoriser l’efficacité de collaboration. Parmi ces facteurs, le collaborateur est un facteur principal, qui est étroitement associé à l’efficacité et à la réussite des collaborati
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Hoque, Tania Tanzin. "A context aware recommender system for tourism with ambient intelligence." Master's thesis, Universidade de Évora, 2020. http://hdl.handle.net/10174/27906.

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Recommender system (RS) holds a significant place in the area of the tourism sector. The major factor of trip planning is selecting relevant Points of Interest (PoI) from tourism domain. The RS system supposed to collect information from user behaviors, personality, preferences and other contextual information. This work is mainly focused on user’s personality, preferences and analyzing user psychological traits. The work is intended to improve the user profile modeling, exposing relationship between user personality and PoI categories and find the solution in constraint satisfaction programmi
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Agagu, Tosin. "Recommendation Approaches Using Context-Aware Coupled Matrix Factorization." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/37012.

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In general, recommender systems attempt to estimate user preference based on historical data. A context-aware recommender system attempts to generate better recommendations using contextual information. However, generating recommendations for specific contexts has been challenging because of the difficulties in using contextual information to enhance the capabilities of recommender systems. Several methods have been used to incorporate contextual information into traditional recommendation algorithms. These methods focus on incorporating contextual information to improve general recommendatio
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Strand, Anton, and Markus Gunnarsson. "Code Reviewer Recommendation : A Context-Aware Hybrid Approach." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18288.

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Background. Code reviewing is a commonly used practice in software development. It refers to the process of reviewing new code changes, commonly before they aremerged with the code base. However, in order to perform the review, developers need to be assigned to that task. The problems with a manual assignment includes a time-consuming selection process; limited pool of known candidates; risk of high reuse of the same reviewers (high workload). Objectives. This thesis aims to attempt to address the above issues with a recommendation system. The idea is to receive feedback from experienced devel
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Thollot, Raphaël. "Dynamic situation monitoring and Context-Aware BI recommendations." Phd thesis, Ecole Centrale Paris, 2012. http://tel.archives-ouvertes.fr/tel-00718917.

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The amount of information generated and maintained by information systems and their users leads to the increasingly important concern of information overload. Personalized systems have thus emerged to help provide more relevant information and services to the user. In particular, recommender systems appeared in the mid 1990's and have since then generated a growing interest in both industry and academia. Besides, context-aware systems have been developed to model, capture and interpret information about the user's situation, generally in dynamic and heterogeneous environments. Decision support
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Alghamdi, Hamzah. "E-Tourism: Context-Aware Points of Interest Finder and Trip Designer." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/35676.

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Many countries depend heavily on tourism for their economic growth. The invention of the web has opened new opportunities for tourists to discover new places and live new adventures. However, the number of possible destinations has become huge and even an entire lifespan would not be enough to visit all of these places. Even for one city, there are a significant number of possible places to visit. Nowadays, searching online to find an interesting place to visit is harder than ever, not because there is a lack of information but rather due to the vast amount of information that can be found.
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Sauer, Christian Severin. "Knowledge elicitation and formalisation for context and explanation-aware computing with case-based recommender systems." Thesis, University of West London, 2016. http://repository.uwl.ac.uk/id/eprint/2226/.

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Case-based reasoning (CBR), as one of the problem solving paradigms in the field of Artificial Intelligence (AI), is an approach to the re-use of experience to solve problem. The aim of this research was to identify and evaluate existing and new approaches to elicit and formalise knowledge for context-aware systems as well as systems that are able to perform explanation-aware computing. The research was centred on systems that employ the specific AI approach of CBR. The research identified positive and negative effects of knowledge formalisation as well as synergies of knowledge formalisation
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Books on the topic "CONTEXT-AWARE RECOMMENDER"

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Impact of Context-Aware Recommender Systems on Habitual Listening Patterns. GRIN Verlag GmbH, 2017.

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1st, Kala K. U., and Nandhini M. 2nd. Deep Learning Model for Categorical Context Adaptation in Sequence-Aware Recommender Systems. INSC International Publisher (IIP), 2021.

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Book chapters on the topic "CONTEXT-AWARE RECOMMENDER"

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Adomavicius, Gediminas, and Alexander Tuzhilin. "Context-Aware Recommender Systems." In Recommender Systems Handbook. Springer US, 2010. http://dx.doi.org/10.1007/978-0-387-85820-3_7.

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Adomavicius, Gediminas, and Alexander Tuzhilin. "Context-Aware Recommender Systems." In Recommender Systems Handbook. Springer US, 2015. http://dx.doi.org/10.1007/978-1-4899-7637-6_6.

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Negre, Elsa, Franck Ravat, and Olivier Teste. "OLAP Queries Context-Aware Recommender System." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98812-2_9.

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Zheng, Yong, Bamshad Mobasher, and Robin Burke. "Emotions in Context-Aware Recommender Systems." In Human–Computer Interaction Series. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31413-6_15.

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Miao, Huiyu, Bingqing Luo, and Zhixin Sun. "An Improved Context-Aware Recommender Algorithm." In Intelligent Computing Theories and Application. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42291-6_15.

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Zhong, Jinfeng, and Elsa Negre. "Context-Aware Explanations in Recommender Systems." In Progresses in Artificial Intelligence & Robotics: Algorithms & Applications. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98531-8_8.

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Inzunza, Sergio, and Reyes Juárez-Ramírez. "A Comprehensive Context-Aware Recommender System Framework." In Computer Science and Engineering—Theory and Applications. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74060-7_1.

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Herzog, Daniel, and Wolfgang Wörndl. "Mobile and Context-Aware Event Recommender Systems." In Lecture Notes in Business Information Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66468-2_8.

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Dixit, Veer Sain, and Parul Jain. "Weighted Percentile-Based Context-Aware Recommender System." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1822-1_35.

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Tang, Tiffany Y., Pinata Winoto, and Gordon McCalla. "Further Thoughts on Context-Aware Paper Recommendations for Education." In Recommender Systems for Technology Enhanced Learning. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0530-0_8.

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Conference papers on the topic "CONTEXT-AWARE RECOMMENDER"

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Nashed, Nader N., Marie-Hélène Abel, and Christine Lahoud. "Context-Aware Hybrid Recommender System for Teachers within MEMORAe SoIS." In 2025 28th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, 2025. https://doi.org/10.1109/cscwd64889.2025.11033644.

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Adomavicius, Gediminas, and Alexander Tuzhilin. "Context-aware recommender systems." In the 2008 ACM conference. ACM Press, 2008. http://dx.doi.org/10.1145/1454008.1454068.

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Abbas, Manzar, Muhammad Usman Riaz, Asad Rauf, Muhammad Taimoor Khan, and Shehzad Khalid. "Context-aware Youtube recommender system." In 2017 International Conference on Information and Communication Technologies (ICICT). IEEE, 2017. http://dx.doi.org/10.1109/icict.2017.8320183.

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Elahi, Mehdi. "Context-aware intelligent recommender system." In the 15th international conference. ACM Press, 2010. http://dx.doi.org/10.1145/1719970.1720045.

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Jain, Rajshree, Jaya Tyagi, Sandeep Kumar Singh, and Taj Alam. "Hybrid context aware recommender systems." In ADVANCEMENT IN MATHEMATICAL SCIENCES: Proceedings of the 2nd International Conference on Recent Advances in Mathematical Sciences and its Applications (RAMSA-2017). Author(s), 2017. http://dx.doi.org/10.1063/1.5008707.

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Unger, Moshe. "Latent Context-Aware Recommender Systems." In RecSys '15: Ninth ACM Conference on Recommender Systems. ACM, 2015. http://dx.doi.org/10.1145/2792838.2796546.

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Ricci, Francesco. "Context-aware music recommender systems." In the 21st international conference companion. ACM Press, 2012. http://dx.doi.org/10.1145/2187980.2188215.

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Kahng, Minsuk, Sangkeun Lee, and Sang-goo Lee. "Ranking in context-aware recommender systems." In the 20th international conference companion. ACM Press, 2011. http://dx.doi.org/10.1145/1963192.1963226.

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Cella, Leonardo. "Efficient Context-Aware Sequential Recommender System." In Companion of the The Web Conference 2018. ACM Press, 2018. http://dx.doi.org/10.1145/3184558.3191581.

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Adomavicius, Gediminas, Konstantin Bauman, Bamshad Mobasher, Francesco Ricci, Alexander Tuzhilin, and Moshe Unger. "Workshop on context-aware recommender systems." In RecSys '19: Thirteenth ACM Conference on Recommender Systems. ACM, 2019. http://dx.doi.org/10.1145/3298689.3346954.

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Reports on the topic "CONTEXT-AWARE RECOMMENDER"

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Li, Richard. LegalOne Blue Ribbon 2024 (China): Highly recommended 15 for dispute resolution. LegalOne Global Limited, 2024. http://dx.doi.org/10.62436/a-1718171324240.

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LegalOne is well aware of this global commonality and regional specificity within the industry landscape. Therefore, we focus not only on the globally common and enduring areas of legal services but also keenly observe the changes and characteristics of the legal services industry in different jurisdictions. Born from this context is the Blue Ribbon award, which aims to recognise lawyers who have achieved outstanding success in the key and prominent areas of the local legal services market over the past year.
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Li, Richard. LegalOne Blue Ribbon 2024 (China): Highly recommended 15 for bankruptcy and restructuring. LegalOne Global Limited, 2024. http://dx.doi.org/10.62436/a-1721619601306.

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LegalOne is well aware of this global commonality and regional specificity within the industry landscape. Therefore, we focus not only on the globally common and enduring areas of legal services but also keenly observe the changes and characteristics of the legal services industry in different jurisdictions. Born from this context is the Blue Ribbon award, which aims to recognise lawyers who have achieved outstanding success in the key and prominent areas of the local legal services market over the past year.
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Li, Richard. LegalOne Blue Ribbon 2024 (China): Highly recommended 15 for business crime and criminal compliance. LegalOne Global Limited, 2024. http://dx.doi.org/10.62436/a-1717126669403.

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LegalOne is well aware of this global commonality and regional specificity within the industry landscape. Therefore, we focus not only on the globally common and enduring areas of legal services but also keenly observe the changes and characteristics of the legal services industry in different jurisdictions. Born from this context is the Blue Ribbon award, which aims to recognise lawyers who have achieved outstanding success in the key and prominent areas of the local legal services market over the past year.
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Li, Richard. LegalOne Blue Ribbon 2024 (China): Highly recommended 15 for intellectual property. LegalOne Global Limited, 2024. http://dx.doi.org/10.62436/a-1724724557999.

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The legal services sector is closely linked to the development and characteristics of society, economy, and various industries. It is therefore continuously evolving and, in the meantime, exhibits regional differences. LegalOne is well aware of this global commonality and regional specificity within the industry landscape. Therefore, we focus not only on the globally common and enduring areas of legal services but also keenly observe the changes and characteristics of the legal services industry in different jurisdictions. Born from this context is the Blue Ribbon award, which aims to recognis
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Li, Richard. LegalOne Blue Ribbon 2024 (China): Highly recommended 15 for M&A and restructuring. LegalOne Global Limited, 2024. http://dx.doi.org/10.62436/a-1724727695313.

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The legal services sector is closely linked to the development and characteristics of society, economy, and various industries. It is therefore continuously evolving and, in the meantime, exhibits regional differences. LegalOne is well aware of this global commonality and regional specificity within the industry landscape. Therefore, we focus not only on the globally common and enduring areas of legal services but also keenly observe the changes and characteristics of the legal services industry in different jurisdictions. Born from this context is the Blue Ribbon award, which aims to recognis
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Li, Richard. LegalOne Blue Ribbon 2024 (China): Highly recommended 15 for government and public affairs. LegalOne Global Limited, 2024. http://dx.doi.org/10.62436/a-1721619648152.

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LegalOne is well aware of this global commonality and regional specificity within the industry landscape. Therefore, we focus not only on the globally common and enduring areas of legal services but also keenly observe the changes and characteristics of the legal services industry in different jurisdictions. Born from this context is the Blue Ribbon award, which aims to recognise lawyers who have achieved outstanding success in the key and prominent areas of the local legal services market over the past year. The scope of this award aligns with current market trends, focusing on the distinct c
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