Littérature scientifique sur le sujet « CONTEXT-AWARE RECOMMENDER »
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Articles de revues sur le sujet "CONTEXT-AWARE RECOMMENDER"
Adomavicius, Gediminas, Bamshad Mobasher, Francesco Ricci et Alexander Tuzhilin. « Context-Aware Recommender Systems ». AI Magazine 32, no 3 (31 octobre 2011) : 67. http://dx.doi.org/10.1609/aimag.v32i3.2364.
Texte intégralWANG, Li-Cai, Xiang-Wu MENG et Yu-Jie ZHANG. « Context-Aware Recommender Systems ». Journal of Software 23, no 1 (5 mars 2012) : 1–20. http://dx.doi.org/10.3724/sp.j.1001.2012.04100.
Texte intégralPisotskyi, Marian, et Alexey Botchkaryov. « Online Video Platform with Context-aware Content-based Recommender System ». Advances in Cyber-Physical Systems 6, no 1 (23 janvier 2021) : 46–53. http://dx.doi.org/10.23939/acps2021.01.046.
Texte intégralQassimi, Sara, El Hassan Abdelwahed et Meriem Hafidi. « Folksonomy Graphs Based Context-Aware Recommender System Using Spectral Clustering ». International Journal of Machine Learning and Computing 10, no 1 (janvier 2020) : 63–68. http://dx.doi.org/10.18178/ijmlc.2020.10.1.899.
Texte intégralKavu, Tatenda D., Kudakwashe Dube et Peter G. Raeth. « Holistic User Context-Aware Recommender Algorithm ». Mathematical Problems in Engineering 2019 (29 septembre 2019) : 1–15. http://dx.doi.org/10.1155/2019/3965845.
Texte intégralAli, Waqar, Jay Kumar, Cobbinah Bernard Mawuli, Lei She et Jie Shao. « Dynamic context management in context-aware recommender systems ». Computers and Electrical Engineering 107 (avril 2023) : 108622. http://dx.doi.org/10.1016/j.compeleceng.2023.108622.
Texte intégralIqbal, Misbah, Mustansar Ali Ghazanfar, Asma Sattar, Muazzam Maqsood, Salabat Khan, Irfan Mehmood et Sung Wook Baik. « 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.
Texte intégralNaha, Sanchita, et 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.
Texte intégralKumar, Rajeev, B. K. Verma et Shyam Sunder Rastogi. « Context-aware Social Popularity based Recommender System ». International Journal of Computer Applications 92, no 2 (18 avril 2014) : 37–42. http://dx.doi.org/10.5120/15985-4907.
Texte intégralWoerndl, Wolfgang, Michele Brocco et Robert Eigner. « Context-Aware Recommender Systems in Mobile Scenarios ». International Journal of Information Technology and Web Engineering 4, no 1 (janvier 2009) : 67–85. http://dx.doi.org/10.4018/jitwe.2009010105.
Texte intégralThèses sur le sujet "CONTEXT-AWARE RECOMMENDER"
Liu, Xiaohu. « Context-aware recommender systems for implicit data ». Thesis, University of York, 2014. http://etheses.whiterose.ac.uk/13237/.
Texte intégralAl-Ghossein, Marie. « Context-aware recommender systems for real-world applications ». Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLT008/document.
Texte intégralRecommender systems have proven to be valuable tools to help users overcome the information overload, and significant advances have been made in the field over the last two decades. In particular, contextual information has been leveraged to model the dynamics occurring within users and items. Context is a complex notion and its traditional definition, which is adopted in most recommender systems, fails to cope with several issues occurring in real-world applications. In this thesis, we address the problems of partially observable and unobservable contexts in two particular applications, hotel recommendation and online recommendation, challenging several aspects of the traditional definition of context, including accessibility, relevance, acquisition, and modeling.The first part of the thesis investigates the problem of hotel recommendation which suffers from the continuous cold-start issue, limiting the performance of classical approaches for recommendation. Traveling is not a frequent activity and users tend to have multifaceted behaviors depending on their specific situation. Following an analysis of the user behavior in this domain, we propose novel recommendation approaches integrating partially observable context affecting users and we show how it contributes in improving the recommendation quality.The second part of the thesis addresses the problem of online adaptive recommendation in streaming environments where data is continuously generated. Users and items may depend on some unobservable context and can evolve in different ways and at different rates. We propose to perform online recommendation by actively detecting drifts and updating models accordingly in real-time. We design novel methods adapting to changes occurring in user preferences, item perceptions, and item descriptions, and show the importance of online adaptive recommendation to ensure a good performance over time
Li, Siying. « Context-aware recommender system for system of information systems ». Thesis, Compiègne, 2021. http://www.theses.fr/2021COMP2602.
Texte intégralWorking collaboratively is no longer an issue but a reality, what matters today is how to implement collaboration so that it is as successful as possible. However, successful collaboration is not easy and is conditioned by different factors that can influence it. It is therefore necessary to take these impacting factors into account within the context of collaboration for promoting the effectiveness of collaboration. Among the impacting factors, collaborator is a main one, which is closely associated with the effectiveness and success of collaborations. The selection and/or recommendation of collaborators, taking into account the context of collaboration, can greatly influence the success of collaboration. Meanwhile, thanks to the development of information technology, many collaborative tools are available, such as e-mail and real-time chat tools. These tools can be integrated into a web-based collaborative work environment. Such environments allow users to collaborate beyond the limit of geographical distances. During collaboration, users can utilize multiple integrated tools, perform various activities, and thus leave traces of activities that can be exploited. This exploitation will be more precise when the context of collaboration is described. It is therefore worth developing web-based collaborative work environments with a model of the collaboration context. Processing the recorded traces can then lead to context-aware collaborator recommendations that can reinforce the collaboration. To generate collaborator recommendations in web-based Collaborative Working Environments, this thesis focuses on producing context-aware collaborator recommendations by defining, modeling, and processing the collaboration context. To achieve this, we first propose a definition of the collaboration context and choose to build a collaboration context ontology given the advantages of the ontology-based modeling approach. Next, an ontologybased semantic similarity is developed and applied in three different algorithms (i.e., PreF1, PoF1, and PoF2) to generate context-aware collaborator recommendations. Furthermore, we deploy the collaboration context ontology into web-based Collaborative Working Environments by considering an architecture of System of Information Systems from the viewpoint of web-based Collaborative Working Environments. Based on this architecture, a corresponding prototype of web-based Collaborative Working Environment is then constructed. Finally, a dataset of scientific collaborations is employed to test and evaluate the performances of the three context-aware collaborator recommendation algorithms
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.
Texte intégralAgagu, Tosin. « Recommendation Approaches Using Context-Aware Coupled Matrix Factorization ». Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/37012.
Texte intégralStrand, Anton, et 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.
Texte intégralThollot, Raphaël. « Dynamic situation monitoring and Context-Aware BI recommendations ». Phd thesis, Ecole Centrale Paris, 2012. http://tel.archives-ouvertes.fr/tel-00718917.
Texte intégralAlghamdi, 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.
Texte intégralSauer, 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/.
Texte intégralAkermi, Imen. « A hybrid model for context-aware proactive recommendation ». Thesis, Toulouse 3, 2017. http://www.theses.fr/2017TOU30101/document.
Texte intégralJust-In-Time recommender systems involve all systems able to provide recommendations tailored to the preferences and needs of users in order to help them access useful and interesting resources within a large data space. The user does not need to formulate a query, this latter is implicit and corresponds to the resources that match the user's interests at the right time. Our work falls within this framework and focuses on developing a proactive context-aware recommendation approach for mobile devices that covers many domains. It aims at recommending relevant items that match users' personal interests at the right time without waiting for the users to initiate any interaction. Indeed, the development of mobile devices equipped with persistent data connections, geolocation, cameras and wireless capabilities allows current context-aware recommender systems (CARS) to be highly contextualized and proactive. We also take into consideration to which degree the recommendation might disturb the user. It is about balancing the process of recommendation against intrusive interruptions. As a matter of fact, there are different factors and situations that make the user less open to recommendations. As we are working within the context of mobile devices, we consider that mobile applications functionalities such as the camera, the keyboard, the agenda, etc., are good representatives of the user's interaction with his device since they somehow stand for most of the activities that a user could use in a mobile device in a daily basis such as texting messages, chatting, tweeting, browsing or taking selfies and pictures
Livres sur le sujet "CONTEXT-AWARE RECOMMENDER"
1st, Kala K. U., et Nandhini M. 2nd. Deep Learning Model for Categorical Context Adaptation in Sequence-Aware Recommender Systems. INSC International Publisher (IIP), 2021.
Trouver le texte intégralChapitres de livres sur le sujet "CONTEXT-AWARE RECOMMENDER"
Adomavicius, Gediminas, et Alexander Tuzhilin. « Context-Aware Recommender Systems ». Dans Recommender Systems Handbook, 217–53. Boston, MA : Springer US, 2010. http://dx.doi.org/10.1007/978-0-387-85820-3_7.
Texte intégralAdomavicius, Gediminas, et Alexander Tuzhilin. « Context-Aware Recommender Systems ». Dans Recommender Systems Handbook, 191–226. Boston, MA : Springer US, 2015. http://dx.doi.org/10.1007/978-1-4899-7637-6_6.
Texte intégralNegre, Elsa, Franck Ravat et Olivier Teste. « OLAP Queries Context-Aware Recommender System ». Dans Lecture Notes in Computer Science, 127–37. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98812-2_9.
Texte intégralZheng, Yong, Bamshad Mobasher et Robin Burke. « Emotions in Context-Aware Recommender Systems ». Dans Human–Computer Interaction Series, 311–26. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31413-6_15.
Texte intégralMiao, Huiyu, Bingqing Luo et Zhixin Sun. « An Improved Context-Aware Recommender Algorithm ». Dans Intelligent Computing Theories and Application, 153–62. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42291-6_15.
Texte intégralZhong, Jinfeng, et Elsa Negre. « Context-Aware Explanations in Recommender Systems ». Dans Progresses in Artificial Intelligence & ; Robotics : Algorithms & ; Applications, 76–85. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98531-8_8.
Texte intégralInzunza, Sergio, et Reyes Juárez-Ramírez. « A Comprehensive Context-Aware Recommender System Framework ». Dans Computer Science and Engineering—Theory and Applications, 1–24. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74060-7_1.
Texte intégralHerzog, Daniel, et Wolfgang Wörndl. « Mobile and Context-Aware Event Recommender Systems ». Dans Lecture Notes in Business Information Processing, 142–63. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66468-2_8.
Texte intégralDixit, Veer Sain, et Parul Jain. « Weighted Percentile-Based Context-Aware Recommender System ». Dans Advances in Intelligent Systems and Computing, 377–88. Singapore : Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1822-1_35.
Texte intégralTang, Tiffany Y., Pinata Winoto et Gordon McCalla. « Further Thoughts on Context-Aware Paper Recommendations for Education ». Dans Recommender Systems for Technology Enhanced Learning, 159–73. New York, NY : Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0530-0_8.
Texte intégralActes de conférences sur le sujet "CONTEXT-AWARE RECOMMENDER"
Adomavicius, Gediminas, et Alexander Tuzhilin. « Context-aware recommender systems ». Dans the 2008 ACM conference. New York, New York, USA : ACM Press, 2008. http://dx.doi.org/10.1145/1454008.1454068.
Texte intégralAbbas, Manzar, Muhammad Usman Riaz, Asad Rauf, Muhammad Taimoor Khan et Shehzad Khalid. « Context-aware Youtube recommender system ». Dans 2017 International Conference on Information and Communication Technologies (ICICT). IEEE, 2017. http://dx.doi.org/10.1109/icict.2017.8320183.
Texte intégralElahi, Mehdi. « Context-aware intelligent recommender system ». Dans the 15th international conference. New York, New York, USA : ACM Press, 2010. http://dx.doi.org/10.1145/1719970.1720045.
Texte intégralJain, Rajshree, Jaya Tyagi, Sandeep Kumar Singh et Taj Alam. « Hybrid context aware recommender systems ». Dans 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.
Texte intégralUnger, Moshe. « Latent Context-Aware Recommender Systems ». Dans RecSys '15 : Ninth ACM Conference on Recommender Systems. New York, NY, USA : ACM, 2015. http://dx.doi.org/10.1145/2792838.2796546.
Texte intégralRicci, Francesco. « Context-aware music recommender systems ». Dans the 21st international conference companion. New York, New York, USA : ACM Press, 2012. http://dx.doi.org/10.1145/2187980.2188215.
Texte intégralKahng, Minsuk, Sangkeun Lee et Sang-goo Lee. « Ranking in context-aware recommender systems ». Dans the 20th international conference companion. New York, New York, USA : ACM Press, 2011. http://dx.doi.org/10.1145/1963192.1963226.
Texte intégralCella, Leonardo. « Efficient Context-Aware Sequential Recommender System ». Dans Companion of the The Web Conference 2018. New York, New York, USA : ACM Press, 2018. http://dx.doi.org/10.1145/3184558.3191581.
Texte intégralAdomavicius, Gediminas, Konstantin Bauman, Bamshad Mobasher, Francesco Ricci, Alexander Tuzhilin et Moshe Unger. « Workshop on context-aware recommender systems ». Dans RecSys '19 : Thirteenth ACM Conference on Recommender Systems. New York, NY, USA : ACM, 2019. http://dx.doi.org/10.1145/3298689.3346954.
Texte intégralPichl, Martin, Eva Zangerle et Günther Specht. « Improving Context-Aware Music Recommender Systems ». Dans ICMR '17 : International Conference on Multimedia Retrieval. New York, NY, USA : ACM, 2017. http://dx.doi.org/10.1145/3078971.3078980.
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