Добірка наукової літератури з теми "EFFICIENT RECOMMENDER SYSTEMS"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "EFFICIENT RECOMMENDER SYSTEMS".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Статті в журналах з теми "EFFICIENT RECOMMENDER SYSTEMS"
Ribeiro, Marco Tulio, Nivio Ziviani, Edleno Silva De Moura, Itamar Hata, Anisio Lacerda, and Adriano Veloso. "Multiobjective Pareto-Efficient Approaches for Recommender Systems." ACM Transactions on Intelligent Systems and Technology 5, no. 4 (January 23, 2015): 1–20. http://dx.doi.org/10.1145/2629350.
Повний текст джерелаHuang, Zhen Hua, Dong Wang, and Sheng Li Sun. "Efficient Mining of Skyrank Items in Recommender Systems." Advanced Materials Research 472-475 (February 2012): 3450–54. http://dx.doi.org/10.4028/www.scientific.net/amr.472-475.3450.
Повний текст джерелаHawashin, Bilal, Shadi Alzubi, Tarek Kanan, and Ayman Mansour. "An efficient semantic recommender method forArabic text." Electronic Library 37, no. 2 (April 1, 2019): 263–80. http://dx.doi.org/10.1108/el-12-2018-0245.
Повний текст джерелаPasdar, Amirmohammad, Young Choon Lee, Tahereh Hassanzadeh, and Khaled Almi’ani. "Resource Recommender for Cloud-Edge Engineering." Information 12, no. 6 (May 25, 2021): 224. http://dx.doi.org/10.3390/info12060224.
Повний текст джерелаJabbar, Muhammad, Qaisar Javaid, Muhammad Arif, Asim Munir, and Ali Javed. "An Efficient and Intelligent Recommender System for Mobile Platform." October 2018 37, no. 4 (October 1, 2018): 463–80. http://dx.doi.org/10.22581/muet1982.1804.02.
Повний текст джерелаRadlinski, Filip, Craig Boutilier, Deepak Ramachandran, and Ivan Vendrov. "Subjective Attributes in Conversational Recommendation Systems: Challenges and Opportunities." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (June 28, 2022): 12287–93. http://dx.doi.org/10.1609/aaai.v36i11.21492.
Повний текст джерелаLuo, Chenhong, Yong Wang, Bo Li, Hanyang Liu, Pengyu Wang, and Leo Yu Zhang. "An Efficient Approach to Manage Natural Noises in Recommender Systems." Algorithms 16, no. 5 (April 27, 2023): 228. http://dx.doi.org/10.3390/a16050228.
Повний текст джерелаCui, Zeyu, Feng Yu, Shu Wu, Qiang Liu, and Liang Wang. "Disentangled Item Representation for Recommender Systems." ACM Transactions on Intelligent Systems and Technology 12, no. 2 (March 2021): 1–20. http://dx.doi.org/10.1145/3445811.
Повний текст джерелаVaidhehi, V., and R. Suchithra. "A Systematic Review of Recommender Systems in Education." International Journal of Engineering & Technology 7, no. 3.4 (June 25, 2018): 188. http://dx.doi.org/10.14419/ijet.v7i3.4.16771.
Повний текст джерелаHawashin, Bilal, Darah Aqel, Shadi Alzubi, and Mohammad Elbes. "Improving Recommender Systems Using Co-Appearing and Semantically Correlated User Interests." Recent Advances in Computer Science and Communications 13, no. 2 (June 3, 2020): 240–47. http://dx.doi.org/10.2174/2213275912666190115162311.
Повний текст джерелаДисертації з теми "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.
Повний текст джерелаSistemas 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.
Повний текст джерелаBroccolo, 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.
Повний текст джерелаLiao, Chih-lun, and 廖志倫. "Efficiency Improvement for Collaborative Filtering Recommender System." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/927hk5.
Повний текст джерела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, and 張益龍. "Algorithms for Improving the Efficiency and Visualization of Library Recommender System." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/40716404095291144017.
Повний текст джерела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, and 曲惠君. "A Study on the Efficiency and Satisfaction of the Library Recommends System." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/63691369948340444917.
Повний текст джерела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.
Повний текст джерелаOs 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.
Книги з теми "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.
Повний текст джерелаConnellan, Geoff. Water Use Efficiency for Irrigated Turf and Landscape. CSIRO Publishing, 2013. http://dx.doi.org/10.1071/9780643106888.
Повний текст джерелаЧастини книг з теми "EFFICIENT RECOMMENDER SYSTEMS"
Ishwarya, M. V., G. Swetha, S. Saptha Maaleekaa, and R. Anu Grahaa. "Efficient Recommender System by Implicit Emotion Prediction." In Advances in Intelligent Systems and Computing, 173–78. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1882-5_15.
Повний текст джерелаKruglov, Artem, Giancarlo Succi, and Anna Gorb. "GQM and Recommender System for Relevant Metrics." In 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.
Повний текст джерелаModarresi, Kourosh, and Jamie Diner. "An Efficient Deep Learning Model for Recommender Systems." In Lecture Notes in Computer Science, 221–33. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93701-4_17.
Повний текст джерелаGao, Yixu, Kun Shao, Zhijian Duan, Zhongyu Wei, Dong Li, Bin Wang, Mengchen Zhao, and Jianye Hao. "Efficient Dual-Process Cognitive Recommender Balancing Accuracy and Diversity." In Database Systems for Advanced Applications, 389–400. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-00129-1_33.
Повний текст джерелаLunawat, Sonali Sagarmal, Abduttayyeb Rampurawala, Sneha Pujari, Siddhi Thawal, Jui Pangare, Chetana Thorat, and Bhushan Munot. "Efficient Recommender System for Kid’s Hobby Using Machine Learning." In Advances in Intelligent Systems and Computing, 327–36. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7088-6_29.
Повний текст джерелаPitsilis, Georgios. "Trust-Enhanced Recommender Systems for Efficient On-Line Collaboration." In Trust Management III, 30–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02056-8_3.
Повний текст джерелаKużelewska, Urszula. "Clustering Algorithms for Efficient Neighbourhood Identification in Session-Based Recommender Systems." In 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.
Повний текст джерелаJeckmans, Arjan, Andreas Peter, and Pieter Hartel. "Efficient Privacy-Enhanced Familiarity-Based Recommender System." In 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.
Повний текст джерелаAymen, Ali Taleb Mohammed, and Saidi Imène. "Scientific Paper Recommender Systems: A Review." In 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.
Повний текст джерелаLuo, Junwei, Xun Yi, Fengling Han, Xuechao Yang, and Xu Yang. "An Efficient Clustering-Based Privacy-Preserving Recommender System." In Network and System Security, 387–405. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-23020-2_22.
Повний текст джерелаТези доповідей конференцій з теми "EFFICIENT RECOMMENDER SYSTEMS"
Bergemann, D., and D. Ozmen. "Efficient Recommender Systems." In 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.
Повний текст джерелаYuan, Weiwei, Sungyoung Lee, Yongkoo Han, Donghai Guan, and Young-Koo Lee. "Efficient routing on finding recommenders for trust-aware recommender systems." In the 6th International Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2184751.2184787.
Повний текст джерелаKelen, Domokos M., Dániel Berecz, Ferenc Béres, and András A. Benczúr. "Efficient K-NN for Playlist Continuation." In the ACM Recommender Systems Challenge 2018. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3267471.3267477.
Повний текст джерелаVu-Thi, Van, Dung Luong-The, and Quan Hoang-Van. "An efficient Privacy-Preserving Recommender System." In 2022 14th International Conference on Knowledge and Systems Engineering (KSE). IEEE, 2022. http://dx.doi.org/10.1109/kse56063.2022.9953800.
Повний текст джерелаZhan, Justin, I.-Cheng Wang, Chia-Lung Hsieh, Tsan-Sheng Hsu, Churn-Jung Liau, and Da-Wei Wang. "Towards efficient privacy-preserving collaborative recommender systems." In 2008 IEEE International Conference on Granular Computing (GrC-2008). IEEE, 2008. http://dx.doi.org/10.1109/grc.2008.4664769.
Повний текст джерелаFaggioli, Guglielmo, Mirko Polato, and Fabio Aiolli. "Efficient Similarity Based Methods For The Playlist Continuation Task." In the ACM Recommender Systems Challenge 2018. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3267471.3267486.
Повний текст джерелаRibeiro, Marco Tulio, Anisio Lacerda, Adriano Veloso, and Nivio Ziviani. "Pareto-efficient hybridization for multi-objective recommender systems." In the sixth ACM conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2365952.2365962.
Повний текст джерелаShahzad, Ahmad, and Frans Coenen. "Efficient Distributed MST Based Clustering for Recommender Systems." In 2020 International Conference on Data Mining Workshops (ICDMW). IEEE, 2020. http://dx.doi.org/10.1109/icdmw51313.2020.00037.
Повний текст джерелаKoutsopoulos, Iordanis, and Maria Halkidi. "Efficient and Fair Item Coverage in Recommender Systems." In 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.
Повний текст джерелаVerhaert, Danilo, Majid Nateghizad, and Zekeriya Erkin. "An Efficient Privacy-preserving Recommender System for e-Healthcare Systems." In International Conference on Security and Cryptography. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0006858501880199.
Повний текст джерелаЗвіти організацій з теми "EFFICIENT RECOMMENDER SYSTEMS"
Wallace, Sean, Scott Lux, Constandinos Mitsingas, Irene Andsager, and 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.), September 2021. http://dx.doi.org/10.21079/11681/42000.
Повний текст джерелаUrban, Angela, Ryan Strange, Andrew Ward, Giselle Rodriguez, and Heidi Howard. Waste management and landfill facilities assessment using unmanned aircraft systems. Engineer Research and Development Center (U.S.), March 2023. http://dx.doi.org/10.21079/11681/46714.
Повний текст джерелаMuelaner, Jody, ed. Unsettled Issues in Commercial Vehicle Platooning. SAE International, November 2021. http://dx.doi.org/10.4271/epr2021027.
Повний текст джерелаHarangozó, 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.
Повний текст джерелаMashingia, 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, November 2021. http://dx.doi.org/10.5703/1288284317429.
Повний текст джерелаChoquette, Gary. PR-000-16209-WEB Data Management Best Practices Learned from CEPM. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), April 2019. http://dx.doi.org/10.55274/r0011568.
Повний текст джерелаKusiak, Chris, Mark D. Bowman, and Arun Prakash. Legal and Permit Loads Evaluation for Indiana Bridges. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317267.
Повний текст джерелаLiu, Zhanjiang John, Rex Dunham, and 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, December 2001. http://dx.doi.org/10.32747/2001.7585196.bard.
Повний текст джерелаDahl, Geoffrey E., Sameer Mabjeesh, Thomas B. McFadden, and Avi Shamay. Environmental manipulation during the dry period of ruminants: strategies to enhance subsequent lactation. United States Department of Agriculture, February 2006. http://dx.doi.org/10.32747/2006.7586544.bard.
Повний текст джерелаDaudelin, Francois, Lina Taing, Lucy Chen, Claudia Abreu Lopes, Adeniyi Francis Fagbamigbe, and Hamid Mehmood. Mapping WASH-related disease risk: A review of risk concepts and methods. United Nations University Institute for Water, Environment and Health, December 2021. http://dx.doi.org/10.53328/uxuo4751.
Повний текст джерела