Literatura académica sobre el tema "CONTEXT-AWARE RECOMMENDER"
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Artículos de revistas sobre el tema "CONTEXT-AWARE RECOMMENDER"
Adomavicius, Gediminas, Bamshad Mobasher, Francesco Ricci y Alexander Tuzhilin. "Context-Aware Recommender Systems". AI Magazine 32, n.º 3 (31 de octubre de 2011): 67. http://dx.doi.org/10.1609/aimag.v32i3.2364.
Texto completoWANG, Li-Cai, Xiang-Wu MENG y Yu-Jie ZHANG. "Context-Aware Recommender Systems". Journal of Software 23, n.º 1 (5 de marzo de 2012): 1–20. http://dx.doi.org/10.3724/sp.j.1001.2012.04100.
Texto completoPisotskyi, Marian y Alexey Botchkaryov. "Online Video Platform with Context-aware Content-based Recommender System". Advances in Cyber-Physical Systems 6, n.º 1 (23 de enero de 2021): 46–53. http://dx.doi.org/10.23939/acps2021.01.046.
Texto completoQassimi, Sara, El Hassan Abdelwahed y Meriem Hafidi. "Folksonomy Graphs Based Context-Aware Recommender System Using Spectral Clustering". International Journal of Machine Learning and Computing 10, n.º 1 (enero de 2020): 63–68. http://dx.doi.org/10.18178/ijmlc.2020.10.1.899.
Texto completoKavu, Tatenda D., Kudakwashe Dube y Peter G. Raeth. "Holistic User Context-Aware Recommender Algorithm". Mathematical Problems in Engineering 2019 (29 de septiembre de 2019): 1–15. http://dx.doi.org/10.1155/2019/3965845.
Texto completoAli, Waqar, Jay Kumar, Cobbinah Bernard Mawuli, Lei She y Jie Shao. "Dynamic context management in context-aware recommender systems". Computers and Electrical Engineering 107 (abril de 2023): 108622. http://dx.doi.org/10.1016/j.compeleceng.2023.108622.
Texto completoIqbal, Misbah, Mustansar Ali Ghazanfar, Asma Sattar, Muazzam Maqsood, Salabat Khan, Irfan Mehmood y 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.
Texto completoNaha, Sanchita y Sudeep Marwaha. "Context-Aware Recommender System for Maize Cultivation". Journal of Community Mobilization and Sustainable Development 15, n.º 2 (2020): 485–90. http://dx.doi.org/10.5958/2231-6736.2020.00034.
Texto completoKumar, Rajeev, B. K. Verma y Shyam Sunder Rastogi. "Context-aware Social Popularity based Recommender System". International Journal of Computer Applications 92, n.º 2 (18 de abril de 2014): 37–42. http://dx.doi.org/10.5120/15985-4907.
Texto completoWoerndl, Wolfgang, Michele Brocco y Robert Eigner. "Context-Aware Recommender Systems in Mobile Scenarios". International Journal of Information Technology and Web Engineering 4, n.º 1 (enero de 2009): 67–85. http://dx.doi.org/10.4018/jitwe.2009010105.
Texto completoTesis sobre el tema "CONTEXT-AWARE RECOMMENDER"
Liu, Xiaohu. "Context-aware recommender systems for implicit data". Thesis, University of York, 2014. http://etheses.whiterose.ac.uk/13237/.
Texto completoAl-Ghossein, Marie. "Context-aware recommender systems for real-world applications". Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLT008/document.
Texto completoRecommender 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.
Texto completoWorking 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.
Texto completoAgagu, Tosin. "Recommendation Approaches Using Context-Aware Coupled Matrix Factorization". Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/37012.
Texto completoStrand, Anton y 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.
Texto completoThollot, Raphaël. "Dynamic situation monitoring and Context-Aware BI recommendations". Phd thesis, Ecole Centrale Paris, 2012. http://tel.archives-ouvertes.fr/tel-00718917.
Texto completoAlghamdi, 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.
Texto completoSauer, 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/.
Texto completoAkermi, Imen. "A hybrid model for context-aware proactive recommendation". Thesis, Toulouse 3, 2017. http://www.theses.fr/2017TOU30101/document.
Texto completoJust-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
Libros sobre el tema "CONTEXT-AWARE RECOMMENDER"
1st, Kala K. U. y Nandhini M. 2nd. Deep Learning Model for Categorical Context Adaptation in Sequence-Aware Recommender Systems. INSC International Publisher (IIP), 2021.
Buscar texto completoCapítulos de libros sobre el tema "CONTEXT-AWARE RECOMMENDER"
Adomavicius, Gediminas y Alexander Tuzhilin. "Context-Aware Recommender Systems". En Recommender Systems Handbook, 217–53. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-0-387-85820-3_7.
Texto completoAdomavicius, Gediminas y Alexander Tuzhilin. "Context-Aware Recommender Systems". En Recommender Systems Handbook, 191–226. Boston, MA: Springer US, 2015. http://dx.doi.org/10.1007/978-1-4899-7637-6_6.
Texto completoNegre, Elsa, Franck Ravat y Olivier Teste. "OLAP Queries Context-Aware Recommender System". En Lecture Notes in Computer Science, 127–37. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98812-2_9.
Texto completoZheng, Yong, Bamshad Mobasher y Robin Burke. "Emotions in Context-Aware Recommender Systems". En Human–Computer Interaction Series, 311–26. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31413-6_15.
Texto completoMiao, Huiyu, Bingqing Luo y Zhixin Sun. "An Improved Context-Aware Recommender Algorithm". En Intelligent Computing Theories and Application, 153–62. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42291-6_15.
Texto completoZhong, Jinfeng y Elsa Negre. "Context-Aware Explanations in Recommender Systems". En 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.
Texto completoInzunza, Sergio y Reyes Juárez-Ramírez. "A Comprehensive Context-Aware Recommender System Framework". En 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.
Texto completoHerzog, Daniel y Wolfgang Wörndl. "Mobile and Context-Aware Event Recommender Systems". En 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.
Texto completoDixit, Veer Sain y Parul Jain. "Weighted Percentile-Based Context-Aware Recommender System". En Advances in Intelligent Systems and Computing, 377–88. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1822-1_35.
Texto completoTang, Tiffany Y., Pinata Winoto y Gordon McCalla. "Further Thoughts on Context-Aware Paper Recommendations for Education". En 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.
Texto completoActas de conferencias sobre el tema "CONTEXT-AWARE RECOMMENDER"
Adomavicius, Gediminas y Alexander Tuzhilin. "Context-aware recommender systems". En the 2008 ACM conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1454008.1454068.
Texto completoAbbas, Manzar, Muhammad Usman Riaz, Asad Rauf, Muhammad Taimoor Khan y Shehzad Khalid. "Context-aware Youtube recommender system". En 2017 International Conference on Information and Communication Technologies (ICICT). IEEE, 2017. http://dx.doi.org/10.1109/icict.2017.8320183.
Texto completoElahi, Mehdi. "Context-aware intelligent recommender system". En the 15th international conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1719970.1720045.
Texto completoJain, Rajshree, Jaya Tyagi, Sandeep Kumar Singh y Taj Alam. "Hybrid context aware recommender systems". En 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.
Texto completoUnger, Moshe. "Latent Context-Aware Recommender Systems". En RecSys '15: Ninth ACM Conference on Recommender Systems. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2792838.2796546.
Texto completoRicci, Francesco. "Context-aware music recommender systems". En the 21st international conference companion. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2187980.2188215.
Texto completoKahng, Minsuk, Sangkeun Lee y Sang-goo Lee. "Ranking in context-aware recommender systems". En the 20th international conference companion. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1963192.1963226.
Texto completoCella, Leonardo. "Efficient Context-Aware Sequential Recommender System". En Companion of the The Web Conference 2018. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3184558.3191581.
Texto completoAdomavicius, Gediminas, Konstantin Bauman, Bamshad Mobasher, Francesco Ricci, Alexander Tuzhilin y Moshe Unger. "Workshop on context-aware recommender systems". En RecSys '19: Thirteenth ACM Conference on Recommender Systems. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3298689.3346954.
Texto completoPichl, Martin, Eva Zangerle y Günther Specht. "Improving Context-Aware Music Recommender Systems". En 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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