Littérature scientifique sur le sujet « EFFICIENT RECOMMENDER SYSTEMS »
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Articles de revues sur le sujet "EFFICIENT RECOMMENDER SYSTEMS"
Ribeiro, Marco Tulio, Nivio Ziviani, Edleno Silva De Moura, Itamar Hata, Anisio Lacerda et Adriano Veloso. « Multiobjective Pareto-Efficient Approaches for Recommender Systems ». ACM Transactions on Intelligent Systems and Technology 5, no 4 (23 janvier 2015) : 1–20. http://dx.doi.org/10.1145/2629350.
Texte intégralHuang, Zhen Hua, Dong Wang et Sheng Li Sun. « Efficient Mining of Skyrank Items in Recommender Systems ». Advanced Materials Research 472-475 (février 2012) : 3450–54. http://dx.doi.org/10.4028/www.scientific.net/amr.472-475.3450.
Texte intégralHawashin, Bilal, Shadi Alzubi, Tarek Kanan et Ayman Mansour. « An efficient semantic recommender method forArabic text ». Electronic Library 37, no 2 (1 avril 2019) : 263–80. http://dx.doi.org/10.1108/el-12-2018-0245.
Texte intégralPasdar, Amirmohammad, Young Choon Lee, Tahereh Hassanzadeh et Khaled Almi’ani. « Resource Recommender for Cloud-Edge Engineering ». Information 12, no 6 (25 mai 2021) : 224. http://dx.doi.org/10.3390/info12060224.
Texte intégralJabbar, Muhammad, Qaisar Javaid, Muhammad Arif, Asim Munir et Ali Javed. « An Efficient and Intelligent Recommender System for Mobile Platform ». October 2018 37, no 4 (1 octobre 2018) : 463–80. http://dx.doi.org/10.22581/muet1982.1804.02.
Texte intégralRadlinski, Filip, Craig Boutilier, Deepak Ramachandran et Ivan Vendrov. « Subjective Attributes in Conversational Recommendation Systems : Challenges and Opportunities ». Proceedings of the AAAI Conference on Artificial Intelligence 36, no 11 (28 juin 2022) : 12287–93. http://dx.doi.org/10.1609/aaai.v36i11.21492.
Texte intégralLuo, Chenhong, Yong Wang, Bo Li, Hanyang Liu, Pengyu Wang et Leo Yu Zhang. « An Efficient Approach to Manage Natural Noises in Recommender Systems ». Algorithms 16, no 5 (27 avril 2023) : 228. http://dx.doi.org/10.3390/a16050228.
Texte intégralCui, Zeyu, Feng Yu, Shu Wu, Qiang Liu et Liang Wang. « Disentangled Item Representation for Recommender Systems ». ACM Transactions on Intelligent Systems and Technology 12, no 2 (mars 2021) : 1–20. http://dx.doi.org/10.1145/3445811.
Texte intégralVaidhehi, V., et R. Suchithra. « A Systematic Review of Recommender Systems in Education ». International Journal of Engineering & ; Technology 7, no 3.4 (25 juin 2018) : 188. http://dx.doi.org/10.14419/ijet.v7i3.4.16771.
Texte intégralHawashin, Bilal, Darah Aqel, Shadi Alzubi et Mohammad Elbes. « Improving Recommender Systems Using Co-Appearing and Semantically Correlated User Interests ». Recent Advances in Computer Science and Communications 13, no 2 (3 juin 2020) : 240–47. http://dx.doi.org/10.2174/2213275912666190115162311.
Texte intégralThèses sur le sujet "EFFICIENT RECOMMENDER SYSTEMS"
Ribeiro, Marco Tulio Correia. « Multi-objective pareto-efficient algorithms for recommender systems ». Universidade Federal de Minas Gerais, 2013. http://hdl.handle.net/1843/ESSA-9CHG5H.
Texte intégralSistemas de recomendação tem se tornado cada vez mais populares em aplicações como e-commerce, mídias sociais e provedores de conteúdo. Esses sistemas agem como mecanismos para lidar com o problema da sobrecarga de informação. Uma tarefa comum em sistemas de recomendação é a de ordenar um conjunto de itens, de forma que os itens no topo da lista sejam de interesse para os usuários. O conceito de interesse pode ser medido observando a acurácia, novidade e diversidade dos itens sugeridos. Geralmente, o objetivo de um sistema de recomendação é gerar listas ordenadas de forma a otimizar uma dessas métricas. Um problema mais difícil é tentar otimizar as três métricas (ou objetivos) simultaneamente, o que pode levar ao caso onde a tentativa de melhorar em uma das métricas pode piorar o resultado nas outras métricas. Neste trabalho, propomos novas abordagens para sistemas de recomendaççao multi-objetivo, baseadas no conceito de Eficiência de Pareto -- um estado obtido quando o sistema é de tal forma que não há como melhorar em algum objetivo sem piorar em outro objetivo. Dado que os algoritmos de recomendação existentes diferem em termos de acurácia, diversidade e novidade, exploramos o conceito de Eficiência de Pareto de duas formas distintas: (i) agregando listas ordenadas produzidas por algoritmos existentes de forma a obter uma lista única - abordagem que chamamos de ranking Pareto-eficiente, e (ii), a combinação linear ponderada de algoritmos existentes, resultado em um híbrido, abordagem que chamamos de hibridização Pareto-eficiente. Nossa avaliação envolve duas aplicações reais: recomendação de música com feedback implícito (i.e., Last.fm) e recomendação de filmes com feedback explícito (i.e., Movielens). Nós mostramos que as abordagens Pareto-eficientes são efetivas em recomendar items com bons niveis de acurácia, novidade e diversidade (simultaneamente), ou uma das métricas sem piorar as outras. Além disso, para a hibridização Pareto-eficiente, provemos uma forma de ajustar o compromisso entre acurácia, novidade e diversidade, de forma que a ênfase da recomendação possa ser ajustada dinamicamente para usuários diferentes.
Esfandiar, Pooya. « Efficient approximation of social relatedness over large social networks and application to query enabled recommender systems ». Thesis, University of British Columbia, 2010. http://hdl.handle.net/2429/27778.
Texte intégralBroccolo, Daniele <1984>. « Query log based techniques to improve the performance of a web search engine ». Doctoral thesis, Università Ca' Foscari Venezia, 2014. http://hdl.handle.net/10579/4635.
Texte intégralLiao, Chih-lun, et 廖志倫. « Efficiency Improvement for Collaborative Filtering Recommender System ». Thesis, 2015. http://ndltd.ncl.edu.tw/handle/927hk5.
Texte intégral國立中山大學
電機工程學系研究所
104
In collaborative filtering based recommender systems, products are regarded as features and users are required to provide rating scores to the products they have purchased. By learning from the rating scores, such a recommender system can recommend interesting products to the users. However, there are usually quite a lot of products involved and it would be very inefficient if every product needs to be considered before making any recommendations. We propose a novel approach which applies a self-constructing clustering algorithm to reduce the dimensionality related to the number of products. Similar products are grouped in a cluster and dissimilar products are dispatched in different clusters. Recommendation work is then done with the resulting clusters. Finally, re-transformation is performed and a preference list about the products is offered to each user. With the proposed approach, the processing time for making recommendations is much reduced. Experimental results show that the efficiency of the recommender systems is greatly improved without the degradation of the recommendation quality.
Chang, I.-Lung, et 張益龍. « Algorithms for Improving the Efficiency and Visualization of Library Recommender System ». Thesis, 2010. http://ndltd.ncl.edu.tw/handle/40716404095291144017.
Texte intégral國立中興大學
資訊科學與工程學系所
98
In the era of information explosion, personalized recommendation services have become an important component for most information systems. Since 2006, the research team mainly consisting of researchers at National Chung-Hsing University (NCHU) proposed a recommender system called PORE (Personal Ontology REcommender System) for recommending library collections. The system is the first one in using personal ontology for library collection recommendation. However, there is still some weakness which should be improved in the PORE system. This thesis proposed methods on improving visualization of recommended results and the performance of collaborative filtering recommendation. For visualization of recommended results, this study developed a 3D interface using an open source tool, OntoSphere 3D, to show personal ontology, the relationship between personal ontology, and the recommended library collections. In the 3D model, users can rotate, shrink, or move recommended objects. As a result, the 3D interface provides a more user-friendly interface to the users of the PORE system. For the performance improvement of collaborative filtering recommendation, we proposed a method that clusters users with overlapping personal ontology and then finds similar users in the group. The experimental results show that the proposed method can save up to 96% of computation.
CHU, HUI CHUN, et 曲惠君. « A Study on the Efficiency and Satisfaction of the Library Recommends System ». Thesis, 2011. http://ndltd.ncl.edu.tw/handle/63691369948340444917.
Texte intégral國立臺灣師範大學
圖書資訊學研究所在職進修碩士班
99
In today's digital era, the library is also the purpose of business to be able to meet the needs of readers, and the library is the personal service has become an important research topic in recent years. Libraries in this research is based on recommendation system as the core of the system performance and user satisfaction assessment research. Readers training data to the source of the theme by hidden technology and social network analysis to explore (SNA) in the process, readers and readers to explore the similarity between the loan interest, information on Readers set the weights of the relevance of the level, readers to know the most adaptive book recommendations list. In addition, through the performance assessment system and reader satisfaction survey questionnaire, the results of two assessments produced by the middle of the gap, and find out how to take the initiative to explore the needs of readers, and provide the information readers need. Through this study, to explore the behavior of readers use the library, not only for library managers in decision-making collection development policies, recommended books, libraries can be provided in individual subject areas of more extensive and practical service performance.
Videira, Jorge Diogo Fontanete. « Recommended KPIs to monitor and improve CMVM organizational performance ». Master's thesis, 2019. http://hdl.handle.net/10400.14/29763.
Texte intégralOs sistemas de gestão de desempenho são uma ferramenta valiosa a que as organizações podem recorrer para garantir que todos os processos e atividades sejam cumpridos de acordo com os objetivos predefinidos. Quando alinhados e bem estruturados, os indicadores-chave de desempenho (KPIs), garantem um efeito positivo duradouro na eficácia externa e na eficiência interna das organizações. Para a CMVM, enquanto instituição que regula e supervisiona o mercado de valores mobiliários portugueses, é indispensável que estes incluam medidas e iniciativas para monitorizar o seu desempenho. Depois de estudar e analisar os diferentes departamentos, bem como tendo recorrido a entrevistas personalizadas em diferentes níveis hierárquicos da organização, esta investigação identificou seis objetivos que são explicados ao longo desta dissertação. Para cada objetivo é recomendado e são fornecidos vários KPIs inspirados nos mais eminentes e reconhecidos reguladores financeiros de todo o mundo. Espera-se assim que a CMVM, com recurso à utilização dos KPIs apresentados nesta pesquisa, possa, através da monitorização e supervisionando regularmente as suas operações e atividades, melhorar o seu desempenho organizacional, sem esquecer a necessidade contínua de benchmarking com as melhores práticas em instituições internacionais similares.
Livres sur le sujet "EFFICIENT RECOMMENDER SYSTEMS"
Veshkurtsev, Yury. THE FOUNDATIONS OF THE THEORY OF CONSTRUCTION OF NEW-GENERATION MODEMS. au : AUS PUBLISHERS, 2022. http://dx.doi.org/10.26526/monography_628a8925151ca0.71125494.
Texte intégralConnellan, Geoff. Water Use Efficiency for Irrigated Turf and Landscape. CSIRO Publishing, 2013. http://dx.doi.org/10.1071/9780643106888.
Texte intégralChapitres de livres sur le sujet "EFFICIENT RECOMMENDER SYSTEMS"
Ishwarya, M. V., G. Swetha, S. Saptha Maaleekaa et R. Anu Grahaa. « Efficient Recommender System by Implicit Emotion Prediction ». Dans Advances in Intelligent Systems and Computing, 173–78. Singapore : Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1882-5_15.
Texte intégralKruglov, Artem, Giancarlo Succi et Anna Gorb. « GQM and Recommender System for Relevant Metrics ». Dans Developing Sustainable and Energy-Efficient Software Systems, 39–46. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11658-2_4.
Texte intégralModarresi, Kourosh, et Jamie Diner. « An Efficient Deep Learning Model for Recommender Systems ». Dans Lecture Notes in Computer Science, 221–33. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93701-4_17.
Texte intégralGao, Yixu, Kun Shao, Zhijian Duan, Zhongyu Wei, Dong Li, Bin Wang, Mengchen Zhao et Jianye Hao. « Efficient Dual-Process Cognitive Recommender Balancing Accuracy and Diversity ». Dans Database Systems for Advanced Applications, 389–400. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-00129-1_33.
Texte intégralLunawat, Sonali Sagarmal, Abduttayyeb Rampurawala, Sneha Pujari, Siddhi Thawal, Jui Pangare, Chetana Thorat et Bhushan Munot. « Efficient Recommender System for Kid’s Hobby Using Machine Learning ». Dans Advances in Intelligent Systems and Computing, 327–36. Singapore : Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7088-6_29.
Texte intégralPitsilis, Georgios. « Trust-Enhanced Recommender Systems for Efficient On-Line Collaboration ». Dans Trust Management III, 30–46. Berlin, Heidelberg : Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02056-8_3.
Texte intégralKużelewska, Urszula. « Clustering Algorithms for Efficient Neighbourhood Identification in Session-Based Recommender Systems ». Dans New Advances in Dependability of Networks and Systems, 143–52. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06746-4_14.
Texte intégralJeckmans, Arjan, Andreas Peter et Pieter Hartel. « Efficient Privacy-Enhanced Familiarity-Based Recommender System ». Dans Lecture Notes in Computer Science, 400–417. Berlin, Heidelberg : Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40203-6_23.
Texte intégralAymen, Ali Taleb Mohammed, et Saidi Imène. « Scientific Paper Recommender Systems : A Review ». Dans Artificial Intelligence and Heuristics for Smart Energy Efficiency in Smart Cities, 896–906. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92038-8_92.
Texte intégralLuo, Junwei, Xun Yi, Fengling Han, Xuechao Yang et Xu Yang. « An Efficient Clustering-Based Privacy-Preserving Recommender System ». Dans Network and System Security, 387–405. Cham : Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-23020-2_22.
Texte intégralActes de conférences sur le sujet "EFFICIENT RECOMMENDER SYSTEMS"
Bergemann, D., et D. Ozmen. « Efficient Recommender Systems ». Dans Proceedings. Joint Conference 8th IEEE International Conference on e-Commerce and Technology/3rd IEEE International Conference on Enterprise Computing, e-Commerce and e-Services/3rd IEEE International Workshop on Mobile Commerce and Wireless Services/Joint Workshop 2nd International Workshop on Business Service Networks/2nd International Workshop On Service Oriented Solutions for Cooperative Organizations. IEEE, 2006. http://dx.doi.org/10.1109/cec-eee.2006.42.
Texte intégralYuan, Weiwei, Sungyoung Lee, Yongkoo Han, Donghai Guan et Young-Koo Lee. « Efficient routing on finding recommenders for trust-aware recommender systems ». Dans the 6th International Conference. New York, New York, USA : ACM Press, 2012. http://dx.doi.org/10.1145/2184751.2184787.
Texte intégralKelen, Domokos M., Dániel Berecz, Ferenc Béres et András A. Benczúr. « Efficient K-NN for Playlist Continuation ». Dans the ACM Recommender Systems Challenge 2018. New York, New York, USA : ACM Press, 2018. http://dx.doi.org/10.1145/3267471.3267477.
Texte intégralVu-Thi, Van, Dung Luong-The et Quan Hoang-Van. « An efficient Privacy-Preserving Recommender System ». Dans 2022 14th International Conference on Knowledge and Systems Engineering (KSE). IEEE, 2022. http://dx.doi.org/10.1109/kse56063.2022.9953800.
Texte intégralZhan, Justin, I.-Cheng Wang, Chia-Lung Hsieh, Tsan-Sheng Hsu, Churn-Jung Liau et Da-Wei Wang. « Towards efficient privacy-preserving collaborative recommender systems ». Dans 2008 IEEE International Conference on Granular Computing (GrC-2008). IEEE, 2008. http://dx.doi.org/10.1109/grc.2008.4664769.
Texte intégralFaggioli, Guglielmo, Mirko Polato et Fabio Aiolli. « Efficient Similarity Based Methods For The Playlist Continuation Task ». Dans the ACM Recommender Systems Challenge 2018. New York, New York, USA : ACM Press, 2018. http://dx.doi.org/10.1145/3267471.3267486.
Texte intégralRibeiro, Marco Tulio, Anisio Lacerda, Adriano Veloso et Nivio Ziviani. « Pareto-efficient hybridization for multi-objective recommender systems ». Dans the sixth ACM conference. New York, New York, USA : ACM Press, 2012. http://dx.doi.org/10.1145/2365952.2365962.
Texte intégralShahzad, Ahmad, et Frans Coenen. « Efficient Distributed MST Based Clustering for Recommender Systems ». Dans 2020 International Conference on Data Mining Workshops (ICDMW). IEEE, 2020. http://dx.doi.org/10.1109/icdmw51313.2020.00037.
Texte intégralKoutsopoulos, Iordanis, et Maria Halkidi. « Efficient and Fair Item Coverage in Recommender Systems ». Dans 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech). IEEE, 2018. http://dx.doi.org/10.1109/dasc/picom/datacom/cyberscitec.2018.000-9.
Texte intégralVerhaert, Danilo, Majid Nateghizad et Zekeriya Erkin. « An Efficient Privacy-preserving Recommender System for e-Healthcare Systems ». Dans International Conference on Security and Cryptography. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0006858501880199.
Texte intégralRapports d'organisations sur le sujet "EFFICIENT RECOMMENDER SYSTEMS"
Wallace, Sean, Scott Lux, Constandinos Mitsingas, Irene Andsager et Tapan Patel. Performance testing and modeling of a transpired ventilation preheat solar wall : performance evaluation of facilities at Fort Drum, NY, and Kansas Air National Guard, Topeka, KS. Engineer Research and Development Center (U.S.), septembre 2021. http://dx.doi.org/10.21079/11681/42000.
Texte intégralUrban, Angela, Ryan Strange, Andrew Ward, Giselle Rodriguez et Heidi Howard. Waste management and landfill facilities assessment using unmanned aircraft systems. Engineer Research and Development Center (U.S.), mars 2023. http://dx.doi.org/10.21079/11681/46714.
Texte intégralMuelaner, Jody, dir. Unsettled Issues in Commercial Vehicle Platooning. SAE International, novembre 2021. http://dx.doi.org/10.4271/epr2021027.
Texte intégralHarangozó, Dániel. Croatia’s defence policy in the shadow of COVID-19 and the Russia-Ukraine war (2020-2023). Magyar Külügyi Intézet, 2023. http://dx.doi.org/10.47683/kkielemzesek.ke-2023.29.
Texte intégralMashingia, Jane, S. Maboko, P. I. Mbwiri, A. Okello, S. I. Ahmada, R. Barayandema, R. Tulba et al. Joint Medicines Regulatory Procedure in the East African Community : Registration Timelines and Way Forward. Purdue University, novembre 2021. http://dx.doi.org/10.5703/1288284317429.
Texte intégralChoquette, Gary. PR-000-16209-WEB Data Management Best Practices Learned from CEPM. Chantilly, Virginia : Pipeline Research Council International, Inc. (PRCI), avril 2019. http://dx.doi.org/10.55274/r0011568.
Texte intégralKusiak, Chris, Mark D. Bowman et Arun Prakash. Legal and Permit Loads Evaluation for Indiana Bridges. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317267.
Texte intégralLiu, Zhanjiang John, Rex Dunham et Boaz Moav. Developmental and Evaluation of Advanced Expression Vectors with Both Enhanced Integration and Stable Expression for Transgenic Farmed Fish. United States Department of Agriculture, décembre 2001. http://dx.doi.org/10.32747/2001.7585196.bard.
Texte intégralDahl, Geoffrey E., Sameer Mabjeesh, Thomas B. McFadden et Avi Shamay. Environmental manipulation during the dry period of ruminants : strategies to enhance subsequent lactation. United States Department of Agriculture, février 2006. http://dx.doi.org/10.32747/2006.7586544.bard.
Texte intégralDaudelin, Francois, Lina Taing, Lucy Chen, Claudia Abreu Lopes, Adeniyi Francis Fagbamigbe et Hamid Mehmood. Mapping WASH-related disease risk : A review of risk concepts and methods. United Nations University Institute for Water, Environment and Health, décembre 2021. http://dx.doi.org/10.53328/uxuo4751.
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