Literatura académica sobre el tema "Recommender Algorithm"
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Artículos de revistas sobre el tema "Recommender Algorithm"
Moses, Sharon J. y L. D. Dhinesh Babu. "Buyagain Grocery Recommender Algorithm for Online Shopping of Grocery and Gourmet Foods". International Journal of Web Services Research 15, n.º 3 (julio de 2018): 1–17. http://dx.doi.org/10.4018/ijwsr.2018070101.
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 completoMali, Mahesh, Dhirendra Mishra y M. Vijayalaxmi. "Benchmarking for Recommender System (MFRISE)". 3C TIC: Cuadernos de desarrollo aplicados a las TIC 11, n.º 2 (29 de diciembre de 2022): 146–56. http://dx.doi.org/10.17993/3ctic.2022.112.146-156.
Texto completoHuang, Jiaquan, Zhen Jia y Peng Zuo. "Improved collaborative filtering personalized recommendation algorithm based on k-means clustering and weighted similarity on the reduced item space". Mathematical Modelling and Control 3, n.º 1 (2023): 39–49. http://dx.doi.org/10.3934/mmc.2023004.
Texto completoMısır, Mustafa y Michèle Sebag. "Alors: An algorithm recommender system". Artificial Intelligence 244 (marzo de 2017): 291–314. http://dx.doi.org/10.1016/j.artint.2016.12.001.
Texto completoCintia Ganesha Putri, Debby, Jenq-Shiou Leu y Pavel Seda. "Design of an Unsupervised Machine Learning-Based Movie Recommender System". Symmetry 12, n.º 2 (21 de enero de 2020): 185. http://dx.doi.org/10.3390/sym12020185.
Texto completoZhang, Heng-Ru, Fan Min, Xu He y Yuan-Yuan Xu. "A Hybrid Recommender System Based on User-Recommender Interaction". Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/145636.
Texto completoKumar Ojha, Rajesh y Dr Bhagirathi Nayak. "Application of Machine Learning in Collaborative Filtering Recommender Systems". International Journal of Engineering & Technology 7, n.º 4.38 (3 de diciembre de 2018): 213. http://dx.doi.org/10.14419/ijet.v7i4.38.24445.
Texto completoLi, Wen-Jun, Yuan-Yuan Xu, Qiang Dong, Jun-Lin Zhou y Yan Fu. "TaDb: A time-aware diffusion-based recommender algorithm". International Journal of Modern Physics C 26, n.º 09 (22 de junio de 2015): 1550102. http://dx.doi.org/10.1142/s0129183115501028.
Texto completoGelvez Garcia, Nancy Yaneth, Jesús Gil-Ruíz y Jhon Fredy Bayona-Navarro. "Optimization of Recommender Systems Using Particle Swarms". Ingeniería 28, Suppl (28 de febrero de 2023): e19925. http://dx.doi.org/10.14483/23448393.19925.
Texto completoTesis sobre el tema "Recommender Algorithm"
ROSSETTI, MARCO. "Advancing Recommender Systems from the Algorithm, Interface and Methodological Perspective". Doctoral thesis, Università degli Studi di Milano-Bicocca, 2015. http://hdl.handle.net/10281/70560.
Texto completoRecommender systems are software components that assist users in finding what they are looking for. They have been applied to all kinds of domains, from ecommerce to news, from music to tourism, exploiting all the information available in order to learn user's preferences and to provide useful recommendations. The broad area of recommender systems has many topics that require a deep understanding and great research efforts. In particular, three main aspects are: algorithms, which are the hidden intelligent components that compute recommendations; interfaces, which are the way in which recommendations are shown to the user; evaluation, which is the methodology to assess the effectiveness of a recommender system. In this dissertation we focus on these aspects guided by three considerations. First, textual content related to items and ratings can be exploited in order to improve several aspects, such as to compute recommendations, provide explanations, understand user's tastes and item's capabilities. Second, time in recommender systems should be considered as it has a great influence on popularity and tastes. Third, offline evaluation protocols are not fully convincing, as they are based on accuracy statistics that do not always reflect real user's preferences. Following these motivations six contributions have been delivered, broadly divided in the integration of concepts and time in recommender systems, the application of the topic model to analyze user reviews and to explain latent factors, and the validation of offline recommendation accuracy measurements.
NARAYANASWAMY, SHRIRAM. "A CONCEPT-BASED FRAMEWORK AND ALGORITHMS FOR RECOMMENDER SYSTEMS". University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1186165016.
Texto completoBora, Prachi Champalal. "Runtime Algorithm Selection For Grid Environments: A Component Based Framework". Thesis, Virginia Tech, 2003. http://hdl.handle.net/10919/33823.
Texto completoMaster of Science
Bora, Prachi. "Runtime Algorithm Selection For Grid Environments: A Component Based Framework". Thesis, Virginia Tech, 2003. http://hdl.handle.net/10919/33823.
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Zhang, Richong. "Probabilistic Approaches to Consumer-generated Review Recommendation". Thèse, Université d'Ottawa / University of Ottawa, 2011. http://hdl.handle.net/10393/19935.
Texto completoYe, Brian y Benny Tieu. "Implementation and Evaluation of a Recommender System Based on the Slope One and the Weighted Slope One Algorithm". Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-166438.
Texto completoRekommendationssystem används idag på många olika hemsidor, och är en mekanism som har syftet att, med noggrannhet, ge en personlig rekommendation av objekt till en mängd olika användare. Ett objekt kan exempelvis vara en film från Netflix. Syftet med denna rapport är att implementera en algoritm som uppfyller fem olika implementationsmål. Målen är enligt följande: algoritmen ska vara enkel att implementera, ha en effektiv tid på dataförfrågan, ge noggranna rekommendationer, sätta låga förväntningar hos användaren samt ska algoritmen inte behöva omfattande förändring vid alternering. Slope One är en förenklad version av linjär regression, och kan även användas till att rekommendera objekt. Genom att använda datamängden från Netflix Prize från 2009 och måttet Root-Mean-Square-Error (RMSE) som en utvärderare, kan Slope One generera en precision på 1.007 enheter. Den viktade Slope One, som tar hänsyn till varje föremåls relevans, genererar en precision på 0.990 enheter. När dessa två algoritmer kombineras, behövs inte större fundamentala ändringar i implementationen av Slope One. En rekommendation av något objekt kan genereras omedelbart med någon av de två algoritmerna, dock krävs det en förberäkningsfas i mekanismen. För att få en rekommendation av implementationen i denna rapport, måste användaren åtminstone ha värderat två objekt.
Sun, Mingxuan. "Visualizing and modeling partial incomplete ranking data". Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45793.
Texto completoGonard, François. "Cold-start recommendation : from Algorithm Portfolios to Job Applicant Matching". Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS121/document.
Texto completoThe need for personalized recommendations is motivated by the overabundance of online information, products, social connections. This typically tackled by recommender systems (RS) that learn users interests from past recorded activities. Another context where recommendation is desirable is when estimating the relevance of an item requires complex reasoning based on experience. Machine learning techniques are good candidates to simulate experience with large amounts of data.The present thesis focuses on the cold-start context in recommendation, i.e. the situation where either a new user desires recommendations or a brand-new item is to be recommended. Since no past interaction is available, RSs have to base their reasoning on side descriptions to form recommendations. Two of such recommendation problems are investigated in this work. Recommender systems designed for the cold-start context are designed.The problem of choosing an optimization algorithm in a portfolio can be cast as a recommendation problem. We propose a two components system combining a per-instance algorithm selector and a sequential scheduler to reduce the optimization cost of a brand-new problem instance and mitigate the risk of optimization failure. Both components are trained with past data to simulate experience, and alternatively optimized to enforce their cooperation. The final system won the Open Algorithm Challenge 2017.Automatic job-applicant matching (JAM) has recently received considerable attention in the recommendation community for applications in online recruitment platforms. We develop specific natural language (NL) modeling techniques and combine them with standard recommendation procedures to leverage past user interactions and the textual descriptions of job positions. The NL and recommendation aspects of the JAM problem are studied on two real-world datasets. The appropriateness of various RSs on applications similar to the JAM problem are discussed
Huang, Zan. "GRAPH-BASED ANALYSIS FOR E-COMMERCE RECOMMENDATION". Diss., Tucson, Arizona : University of Arizona, 2005. http://etd.library.arizona.edu/etd/GetFileServlet?file=file:///data1/pdf/etd/azu%5Fetd%5F1167%5F1%5Fm.pdf&type=application/pdf.
Texto completoRedpath, Jennifer Louise. "Improving the performance of recommender algorithms". Thesis, Ulster University, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.535143.
Texto completoLibros sobre el tema "Recommender Algorithm"
Gündüz-Ögüdücü, Şule. Web page recommendation models: Theory and algorithms. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2011.
Buscar texto completoMandra, Yuliya, Elena Semencova, Sergey Griroriev, N. Gegalina, Elena Svetlakova, Maria Vlasova, Yuriy Boldyrev, Anastasiya Kotikova, Aleksandr Ivashov y Aleksandr Legkih. MODERN METHODS OF COMPLEX TREATMENT OF PATIENTS WITH HERPES SIMPLEX LIPS. ru: TIRAZH Publishing House, 2019. http://dx.doi.org/10.18481/textbook_5dfa340500ebf6.85792235.
Texto completoVarlamov, Oleg. 18 examples of mivar expert systems. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1248446.
Texto completoBleakley, Chris. Poems That Solve Puzzles. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198853732.001.0001.
Texto completoMohanty, Sachi Nandan, P. Pavan Kumar, S. Vairachilai y Sirisha Potluri. Recommender Systems: Algorithms and Applications. Taylor & Francis Group, 2021.
Buscar texto completoMohanty, Sachi Nandan, P. Pavan Kumar, S. Vairachilai y Sirisha Potluri. Recommender Systems: Algorithms and Applications. Taylor & Francis Group, 2021.
Buscar texto completoMohanty, Sachi Nandan, P. Pavan Kumar, S. Vairachilai y Sirisha Potluri. Recommender Systems: Algorithms and Applications. Taylor & Francis Group, 2021.
Buscar texto completoTowards Metadata-Aware Algorithms for Recommender Systems. Lang GmbH, Internationaler Verlag der Wissenschaften, Peter, 2010.
Buscar texto completoKant, Tanya. Making it Personal. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190905088.001.0001.
Texto completoBig Data Recommender Systems: Algorithms, Architectures, Big Data, Security and Trust. Institution of Engineering & Technology, 2019.
Buscar texto completoCapítulos de libros sobre el tema "Recommender Algorithm"
Kulkarni, Akshay, Adarsha Shivananda, Anoosh Kulkarni y V. Adithya Krishnan. "Classification Algorithm–Based Recommender Systems". En Applied Recommender Systems with Python, 175–206. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8954-9_8.
Texto completoWang, Po-Kai, Chao-Fu Hong y Min-Huei Lin. "Interactive Genetic Algorithm Joining Recommender System". En Intelligent Information and Database Systems, 40–48. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14802-7_4.
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 completoChen, Jie, Baohua Qiang, Yaoguang Wang, Peng Wang y Jun Huang. "An Optimized Tag Recommender Algorithm in Folksonomy". En Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 47–56. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-662-44980-6_6.
Texto completoLin, Pei-Chun y Nureize Arbaiy. "An Algorithm Design of Kansei Recommender System". En Advances in Intelligent Systems and Computing, 115–23. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-72550-5_12.
Texto completoKról, Dariusz, Zuzanna Zborowska, Paweł Ropa y Łukasz Kincel. "CORDIS Partner Matching Algorithm for Recommender Systems". En Intelligent Information and Database Systems, 701–15. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-21743-2_56.
Texto completoPolatidis, Nikolaos, Stelios Kapetanakis y Elias Pimenidis. "Recommender Systems Algorithm Selection Using Machine Learning". En Proceedings of the International Neural Networks Society, 477–87. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80568-5_39.
Texto completoSingh, Suraj Pal y Shano Solanki. "Recommender System Survey: Clustering to Nature Inspired Algorithm". En Proceedings of 2nd International Conference on Communication, Computing and Networking, 757–68. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1217-5_76.
Texto completoChang, Na, Mhd Irvan y Takao Terano. "An Item Influence-Centric Algorithm for Recommender Systems". En Advances in Intelligent Systems and Computing, 553–60. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07593-8_64.
Texto completoVaishampayan, Samarth, Gururaj Singh, Vinayakprasad Hebasur y Rupali Kute. "Market Basket Analysis Recommender System using Apriori Algorithm". En Lecture Notes in Electrical Engineering, 461–72. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9885-9_38.
Texto completoActas de conferencias sobre el tema "Recommender Algorithm"
De Pessemier, Toon, Kris Vanhecke y Luc Martens. "A scalable, high-performance Algorithm for hybrid job recommendations". En the Recommender Systems Challenge. New York, New York, USA: ACM Press, 2016. http://dx.doi.org/10.1145/2987538.2987539.
Texto completoZhu, Xue y Yuqing Sun. "Differential Privacy for Collaborative Filtering Recommender Algorithm". En CODASPY'16: Sixth ACM Conference on Data and Application Security and Privacy. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2875475.2875483.
Texto completoYaoning Fang y Yunfei Guo. "A context-aware matrix factorization recommender algorithm". En 2013 IEEE 4th International Conference on Software Engineering and Service Science (ICSESS). IEEE, 2013. http://dx.doi.org/10.1109/icsess.2013.6615454.
Texto completoGupta, Utkarsh y Nagamma Patil. "Recommender system based on Hierarchical Clustering algorithm Chameleon". En 2015 IEEE International Advance Computing Conference (IACC). IEEE, 2015. http://dx.doi.org/10.1109/iadcc.2015.7154856.
Texto completoZhang, Liyan, Kai Zhang y Chunping Li. "A topical PageRank based algorithm for recommender systems". En the 31st annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1390334.1390465.
Texto completoLuo, Yongen, Jicheng Hu y Xiaofeng Wei. "Blog Recommender Based on Hypergraph Modeling Clustering Algorithm". En 2013 Fourth World Congress on Software Engineering (WCSE). IEEE, 2013. http://dx.doi.org/10.1109/wcse.2013.42.
Texto completoBansal, Saumya y Niyati Baliyan. "Memetic Algorithm based Similarity Metric for Recommender System". En the 12th IEEE/ACM International Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3368235.3369372.
Texto completoXiong, Lei, Yang Xiang, Qi Zhang y Lili Lin. "A Novel Nearest Neighborhood Algorithm for Recommender Systems". En 2012 Third Global Congress on Intelligent Systems (GCIS). IEEE, 2012. http://dx.doi.org/10.1109/gcis.2012.58.
Texto completoXuan, Zhaoguo, Haoxiang Xia y Jing Miao. "A Personalized Recommender Algorithm Based on Semantic Tree". En 2011 Fourth International Joint Conference on Computational Sciences and Optimization (CSO). IEEE, 2011. http://dx.doi.org/10.1109/cso.2011.52.
Texto completoLiu, T. y D. Fan. "Random Forest Algorithm in Information Personalization Recommender System". En The International Conference on Forthcoming Networks and Sustainability (FoNeS 2022). Institution of Engineering and Technology, 2022. http://dx.doi.org/10.1049/icp.2022.2367.
Texto completoInformes sobre el tema "Recommender Algorithm"
Daudelin, Francois, Lina Taing, Lucy Chen, Claudia Abreu Lopes, Adeniyi Francis Fagbamigbe y Hamid Mehmood. Mapping WASH-related disease risk: A review of risk concepts and methods. United Nations University Institute for Water, Environment and Health, diciembre de 2021. http://dx.doi.org/10.53328/uxuo4751.
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