Academic literature on the topic 'Recommender Algorithm'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Recommender Algorithm.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "Recommender Algorithm"
Moses, Sharon J., and L. D. Dhinesh Babu. "Buyagain Grocery Recommender Algorithm for Online Shopping of Grocery and Gourmet Foods." International Journal of Web Services Research 15, no. 3 (July 2018): 1–17. http://dx.doi.org/10.4018/ijwsr.2018070101.
Full textKavu, Tatenda D., Kudakwashe Dube, and Peter G. Raeth. "Holistic User Context-Aware Recommender Algorithm." Mathematical Problems in Engineering 2019 (September 29, 2019): 1–15. http://dx.doi.org/10.1155/2019/3965845.
Full textMali, Mahesh, Dhirendra Mishra, and M. Vijayalaxmi. "Benchmarking for Recommender System (MFRISE)." 3C TIC: Cuadernos de desarrollo aplicados a las TIC 11, no. 2 (December 29, 2022): 146–56. http://dx.doi.org/10.17993/3ctic.2022.112.146-156.
Full textHuang, Jiaquan, Zhen Jia, and 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, no. 1 (2023): 39–49. http://dx.doi.org/10.3934/mmc.2023004.
Full textMısır, Mustafa, and Michèle Sebag. "Alors: An algorithm recommender system." Artificial Intelligence 244 (March 2017): 291–314. http://dx.doi.org/10.1016/j.artint.2016.12.001.
Full textCintia Ganesha Putri, Debby, Jenq-Shiou Leu, and Pavel Seda. "Design of an Unsupervised Machine Learning-Based Movie Recommender System." Symmetry 12, no. 2 (January 21, 2020): 185. http://dx.doi.org/10.3390/sym12020185.
Full textZhang, Heng-Ru, Fan Min, Xu He, and 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.
Full textKumar Ojha, Rajesh, and Dr Bhagirathi Nayak. "Application of Machine Learning in Collaborative Filtering Recommender Systems." International Journal of Engineering & Technology 7, no. 4.38 (December 3, 2018): 213. http://dx.doi.org/10.14419/ijet.v7i4.38.24445.
Full textLi, Wen-Jun, Yuan-Yuan Xu, Qiang Dong, Jun-Lin Zhou, and Yan Fu. "TaDb: A time-aware diffusion-based recommender algorithm." International Journal of Modern Physics C 26, no. 09 (June 22, 2015): 1550102. http://dx.doi.org/10.1142/s0129183115501028.
Full textGelvez Garcia, Nancy Yaneth, Jesús Gil-Ruíz, and Jhon Fredy Bayona-Navarro. "Optimization of Recommender Systems Using Particle Swarms." Ingeniería 28, Suppl (February 28, 2023): e19925. http://dx.doi.org/10.14483/23448393.19925.
Full textDissertations / Theses on the topic "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.
Full textRecommender 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.
Full textBora, Prachi Champalal. "Runtime Algorithm Selection For Grid Environments: A Component Based Framework." Thesis, Virginia Tech, 2003. http://hdl.handle.net/10919/33823.
Full textMaster of Science
Bora, Prachi. "Runtime Algorithm Selection For Grid Environments: A Component Based Framework." Thesis, Virginia Tech, 2003. http://hdl.handle.net/10919/33823.
Full textMaster of Science
Zhang, Richong. "Probabilistic Approaches to Consumer-generated Review Recommendation." Thèse, Université d'Ottawa / University of Ottawa, 2011. http://hdl.handle.net/10393/19935.
Full textYe, Brian, and 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.
Full textRekommendationssystem 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.
Full textGonard, François. "Cold-start recommendation : from Algorithm Portfolios to Job Applicant Matching." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS121/document.
Full textThe 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.
Full textRedpath, Jennifer Louise. "Improving the performance of recommender algorithms." Thesis, Ulster University, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.535143.
Full textBooks on the topic "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.
Find full textMandra, Yuliya, Elena Semencova, Sergey Griroriev, N. Gegalina, Elena Svetlakova, Maria Vlasova, Yuriy Boldyrev, Anastasiya Kotikova, Aleksandr Ivashov, and 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.
Full textVarlamov, Oleg. 18 examples of mivar expert systems. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1248446.
Full textBleakley, Chris. Poems That Solve Puzzles. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198853732.001.0001.
Full textMohanty, Sachi Nandan, P. Pavan Kumar, S. Vairachilai, and Sirisha Potluri. Recommender Systems: Algorithms and Applications. Taylor & Francis Group, 2021.
Find full textMohanty, Sachi Nandan, P. Pavan Kumar, S. Vairachilai, and Sirisha Potluri. Recommender Systems: Algorithms and Applications. Taylor & Francis Group, 2021.
Find full textMohanty, Sachi Nandan, P. Pavan Kumar, S. Vairachilai, and Sirisha Potluri. Recommender Systems: Algorithms and Applications. Taylor & Francis Group, 2021.
Find full textTowards Metadata-Aware Algorithms for Recommender Systems. Lang GmbH, Internationaler Verlag der Wissenschaften, Peter, 2010.
Find full textKant, Tanya. Making it Personal. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190905088.001.0001.
Full textBig Data Recommender Systems: Algorithms, Architectures, Big Data, Security and Trust. Institution of Engineering & Technology, 2019.
Find full textBook chapters on the topic "Recommender Algorithm"
Kulkarni, Akshay, Adarsha Shivananda, Anoosh Kulkarni, and V. Adithya Krishnan. "Classification Algorithm–Based Recommender Systems." In Applied Recommender Systems with Python, 175–206. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8954-9_8.
Full textWang, Po-Kai, Chao-Fu Hong, and Min-Huei Lin. "Interactive Genetic Algorithm Joining Recommender System." In Intelligent Information and Database Systems, 40–48. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14802-7_4.
Full textMiao, Huiyu, Bingqing Luo, and Zhixin Sun. "An Improved Context-Aware Recommender Algorithm." In Intelligent Computing Theories and Application, 153–62. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42291-6_15.
Full textChen, Jie, Baohua Qiang, Yaoguang Wang, Peng Wang, and Jun Huang. "An Optimized Tag Recommender Algorithm in Folksonomy." In 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.
Full textLin, Pei-Chun, and Nureize Arbaiy. "An Algorithm Design of Kansei Recommender System." In 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.
Full textKról, Dariusz, Zuzanna Zborowska, Paweł Ropa, and Łukasz Kincel. "CORDIS Partner Matching Algorithm for Recommender Systems." In Intelligent Information and Database Systems, 701–15. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-21743-2_56.
Full textPolatidis, Nikolaos, Stelios Kapetanakis, and Elias Pimenidis. "Recommender Systems Algorithm Selection Using Machine Learning." In 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.
Full textSingh, Suraj Pal, and Shano Solanki. "Recommender System Survey: Clustering to Nature Inspired Algorithm." In 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.
Full textChang, Na, Mhd Irvan, and Takao Terano. "An Item Influence-Centric Algorithm for Recommender Systems." In 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.
Full textVaishampayan, Samarth, Gururaj Singh, Vinayakprasad Hebasur, and Rupali Kute. "Market Basket Analysis Recommender System using Apriori Algorithm." In Lecture Notes in Electrical Engineering, 461–72. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9885-9_38.
Full textConference papers on the topic "Recommender Algorithm"
De Pessemier, Toon, Kris Vanhecke, and Luc Martens. "A scalable, high-performance Algorithm for hybrid job recommendations." In the Recommender Systems Challenge. New York, New York, USA: ACM Press, 2016. http://dx.doi.org/10.1145/2987538.2987539.
Full textZhu, Xue, and Yuqing Sun. "Differential Privacy for Collaborative Filtering Recommender Algorithm." In 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.
Full textYaoning Fang and Yunfei Guo. "A context-aware matrix factorization recommender algorithm." In 2013 IEEE 4th International Conference on Software Engineering and Service Science (ICSESS). IEEE, 2013. http://dx.doi.org/10.1109/icsess.2013.6615454.
Full textGupta, Utkarsh, and Nagamma Patil. "Recommender system based on Hierarchical Clustering algorithm Chameleon." In 2015 IEEE International Advance Computing Conference (IACC). IEEE, 2015. http://dx.doi.org/10.1109/iadcc.2015.7154856.
Full textZhang, Liyan, Kai Zhang, and Chunping Li. "A topical PageRank based algorithm for recommender systems." In the 31st annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1390334.1390465.
Full textLuo, Yongen, Jicheng Hu, and Xiaofeng Wei. "Blog Recommender Based on Hypergraph Modeling Clustering Algorithm." In 2013 Fourth World Congress on Software Engineering (WCSE). IEEE, 2013. http://dx.doi.org/10.1109/wcse.2013.42.
Full textBansal, Saumya, and Niyati Baliyan. "Memetic Algorithm based Similarity Metric for Recommender System." In the 12th IEEE/ACM International Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3368235.3369372.
Full textXiong, Lei, Yang Xiang, Qi Zhang, and Lili Lin. "A Novel Nearest Neighborhood Algorithm for Recommender Systems." In 2012 Third Global Congress on Intelligent Systems (GCIS). IEEE, 2012. http://dx.doi.org/10.1109/gcis.2012.58.
Full textXuan, Zhaoguo, Haoxiang Xia, and Jing Miao. "A Personalized Recommender Algorithm Based on Semantic Tree." In 2011 Fourth International Joint Conference on Computational Sciences and Optimization (CSO). IEEE, 2011. http://dx.doi.org/10.1109/cso.2011.52.
Full textLiu, T., and D. Fan. "Random Forest Algorithm in Information Personalization Recommender System." In 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.
Full textReports on the topic "Recommender Algorithm"
Daudelin, 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.
Full text