Academic literature on the topic 'Ranking to Learn'
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Journal articles on the topic "Ranking to Learn"
Yu, Yonghong, Li Zhang, Can Wang, Rong Gao, Weibin Zhao, and Jing Jiang. "Neural Personalized Ranking via Poisson Factor Model for Item Recommendation." Complexity 2019 (January 3, 2019): 1–16. http://dx.doi.org/10.1155/2019/3563674.
Full textLi, Xiaoming, Hui Fang, and Jie Zhang. "Supervised User Ranking in Signed Social Networks." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 184–91. http://dx.doi.org/10.1609/aaai.v33i01.3301184.
Full textDzyuba, Vladimir, Matthijs van Leeuwen, Siegfried Nijssen, and Luc De Raedt. "Interactive Learning of Pattern Rankings." International Journal on Artificial Intelligence Tools 23, no. 06 (December 2014): 1460026. http://dx.doi.org/10.1142/s0218213014600264.
Full textZeng, Kaiman, Nansong Wu, Arman Sargolzaei, and Kang Yen. "Learn to Rank Images: A Unified Probabilistic Hypergraph Model for Visual Search." Mathematical Problems in Engineering 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/7916450.
Full textUdupi, Prakash Kumar, Vishal Dattana, P. S. Netravathi, and Jitendra Pandey. "Predicting Global Ranking of Universities Across the World Using Machine Learning Regression Technique." SHS Web of Conferences 156 (2023): 04001. http://dx.doi.org/10.1051/shsconf/202315604001.
Full textGao, Wei, and Yun Gang Zhang. "Generalization Bounds for Certain Class of Ranking Algorithm." Advanced Materials Research 267 (June 2011): 456–61. http://dx.doi.org/10.4028/www.scientific.net/amr.267.456.
Full textWu, Buchen, and Jiwei Qin. "A List-Ranking Framework Based on Linear and Non-Linear Fusion for Recommendation from Implicit Feedback." Entropy 24, no. 6 (May 31, 2022): 778. http://dx.doi.org/10.3390/e24060778.
Full textZhang, Wei, Zeyuan Chen, Chao Dong, Wen Wang, Hongyuan Zha, and Jianyong Wang. "Graph-Based Tri-Attention Network for Answer Ranking in CQA." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 16 (May 18, 2021): 14463–71. http://dx.doi.org/10.1609/aaai.v35i16.17700.
Full textOosterhuis, Harrie. "Learning from user interactions with rankings." ACM SIGIR Forum 54, no. 2 (December 2020): 1–2. http://dx.doi.org/10.1145/3483382.3483402.
Full textFarias, Vivek, Srikanth Jagabathula, and Devavrat Shah. "Inferring Sparse Preference Lists from Partial Information." Stochastic Systems 10, no. 4 (December 2020): 335–60. http://dx.doi.org/10.1287/stsy.2019.0060.
Full textDissertations / Theses on the topic "Ranking to Learn"
Lin, Xiao. "Leveraging Multimodal Perspectives to Learn Common Sense for Vision and Language Tasks." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/79521.
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ROFFO, GIORGIO. "Ranking to Learn and Learning to Rank: On the Role of Ranking in Pattern Recognition Applications." Doctoral thesis, 2017. http://hdl.handle.net/11562/960962.
Full textSandupatla, Hareesh. "Using reinforcement learning to learn relevance ranking of search queries." Thesis, 2016. http://hdl.handle.net/1805/11008.
Full textWeb search has become a part of everyday life for hundreds of millions of users around the world. However, the effectiveness of a user's search depends vitally on the quality of search result ranking. Even though enormous efforts have been made to improve the ranking quality, there is still significant misalignment between search engine ranking and an end user's preference order. This is evident from the fact that, for many search results on major search and e-commerce platforms, many users ignore the top ranked results and click on the lower ranked results. Nevertheless, finding a ranking that suits all the users is a difficult problem to solve as every user's need is different. So, an ideal ranking is the one which is preferred by the majority of the users. This emphasizes the need for an automated approach which improves the search engine ranking dynamically by incorporating user clicks in the ranking algorithm. In existing search result ranking methodologies, this direction has not been explored profoundly. A key challenge in using user clicks in search result ranking is that the relevance feedback that is learnt from click data is imperfect. This is due to the fact that a user is more likely to click a top ranked result than a lower ranked result, irrespective of the actual relevance of those results. This phenomenon is known as position bias which poses a major difficulty in obtaining an automated method for dynamic update of search rank orders. In my thesis, I propose a set of methodologies which incorporate user clicks for dynamic update of search rank orders. The updates are based on adaptive randomization of results using reinforcement learning strategy by considering the user click activities as reinforcement signal. Beginning at any rank order of the search results, the proposed methodologies guaranty to converge to a ranking which is close to the ideal rank order. Besides, the usage of reinforcement learning strategy enables the proposed methods to overcome the position bias phenomenon. To measure the effectiveness of the proposed method, I perform experiments considering a simplified user behavior model which I call color ball abstraction model. I evaluate the quality of the proposed methodologies using standard information retrieval metrics like Precision at n (P@n), Kendall tau rank correlation, Discounted Cumulative Gain (DCG) and Normalized Discounted Cumulative Gain (NDCG). The experiment results clearly demonstrate the success of the proposed methodologies.
Books on the topic "Ranking to Learn"
Smith, Lasse Martin. Solid Ranking : Search Engine Optimization: Learn SEO - Search Engine Optimization. CreateSpace Independent Publishing Platform, 2015.
Find full textDonnelly, Kevon. Seo Blueprint: Learn the Secrets to Improve Your Ranking and Surpass Your Competitors. Independently Published, 2019.
Find full textIgnite Your Linkedin Profile: Learn the Secrets to How Linkedin Ranking Really Works. Wittman Technology, LLC, 2019.
Find full textDenis, Benjamin. Master Guide : SEO for Business: Learn How to Improve Your Ranking with Local Business, WooCommerce and Structured Data Types. Independently Published, 2022.
Find full textPrints, Cloud. Search Engine Optimization Strategies: Learn the Unique Strategies for Researching and Using High-Ranking Keywords to Rank First in Search Engines // Internet Search Engine. Independently Published, 2022.
Find full textTorbert, Adam. Digital Marketing Essentials: Learn about Digital Marketing and How to Use It to Leverage Technology to Get More Traffic, Boost Your Website Ranking and Build a Brand. Independently Published, 2019.
Find full textJoseph, Kelly. Self Publishing, SEO and Social Media Marketing Guides : : Learn from a Best Seller How to Write, Publish and Market Best Selling Books on Facebook, Optimize Your Product's Search Engines Ranking. Independently Published, 2017.
Find full textStorm, Thomas. Link Building for Beginners: Learn how to build links and improve your rankings. Createspace Independent Publishing Platform, 2016.
Find full textGeasey, Richard. Get Found Now! Local Search Secrets Exposed: Learn How to Achieve High Rankings in Google, Yahoo and Bing. CreateSpace Independent Publishing Platform, 2009.
Find full textLocke, Joseph. The Road to the Bible Belt. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190216283.003.0005.
Full textBook chapters on the topic "Ranking to Learn"
Roffo, Giorgio, and Simone Melzi. "Ranking to Learn:." In New Frontiers in Mining Complex Patterns, 19–35. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61461-8_2.
Full textBorgulya, István. "Learn the Ranking of Precedence Cases." In The State of the Art in Computational Intelligence, 152–61. Heidelberg: Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1844-4_26.
Full textHerbst, Patricio. "Geometric Modeling Tasks and Opportunity to Learn Geometry: The Ranking Triangles Task Revisited." In Research in Mathematics Education, 123–43. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29215-7_7.
Full textErkkilä, Tero, and Ossi Piironen. "What Counts as World Class? Global University Rankings and Shifts in Institutional Strategies." In Evaluating Education: Normative Systems and Institutional Practices, 171–96. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7598-3_11.
Full textCavenaghi, Emanuele, Lorenzo Camaione, Paolo Minasi, Gabriele Sottocornola, Fabio Stella, and Markus Zanker. "A Re-rank Algorithm for Online Hotel Search." In Information and Communication Technologies in Tourism 2023, 53–64. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25752-0_5.
Full textChahal, Virender, M. S. Narwal, and Sachin Kumar. "Ranking of Lean Critical Success Factors in Manufacturing Industry: AHP Approach." In Advances in Materials and Mechanical Engineering, 411–19. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0673-1_34.
Full textSchmidt, William, Nathan Burroughs, Lee Cogan, and Richard Houang. "Are College Rankings an Indicator of Quality Education? Comparing Barron’s and TEDS-M." In International Perspectives on Teacher Knowledge, Beliefs and Opportunities to Learn, 503–14. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-6437-8_23.
Full textSindhwani, Rahul, Punj Lata Singh, Vipin Kaushik, Sumit Sharma, Rakesh Kumar Phanden, and Devendra Kumar Prajapati. "Ranking of Factors for Integrated Lean, Green and Agile Manufacturing for Indian Manufacturing SMEs." In Lecture Notes in Mechanical Engineering, 203–19. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4565-8_18.
Full textAbate, Alessandro, Mirco Giacobbe, and Diptarko Roy. "Learning Probabilistic Termination Proofs." In Computer Aided Verification, 3–26. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81688-9_1.
Full textScott, Joseph, Aina Niemetz, Mathias Preiner, Saeed Nejati, and Vijay Ganesh. "MachSMT: A Machine Learning-based Algorithm Selector for SMT Solvers." In Tools and Algorithms for the Construction and Analysis of Systems, 303–25. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72013-1_16.
Full textConference papers on the topic "Ranking to Learn"
Bertolino, Antonia, Antonio Guerriero, Breno Miranda, Roberto Pietrantuono, and Stefano Russo. "Learning-to-rank vs ranking-to-learn." In ICSE '20: 42nd International Conference on Software Engineering. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3377811.3380369.
Full textConnes, Victor, Colin de la Higuera, and Hoel Le Capitaine. "What should I learn next? Ranking Educational Resources." In 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE, 2021. http://dx.doi.org/10.1109/compsac51774.2021.00026.
Full textEngilberge, Martin, Louis Chevallier, Patrick Perez, and Matthieu Cord. "SoDeep: A Sorting Deep Net to Learn Ranking Loss Surrogates." In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.01105.
Full textWanigasekara, Nirandika, Yuxuan Liang, Siong Thye Goh, Ye Liu, Joseph Jay Williams, and David S. Rosenblum. "Learning Multi-Objective Rewards and User Utility Function in Contextual Bandits for Personalized Ranking." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/532.
Full textChen, Xuanang, Jian Luo, Ben He, Le Sun, and Yingfei Sun. "Towards Robust Dense Retrieval via Local Ranking Alignment." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/275.
Full textKim, Yejin, Kwangseob Kim, Chanyoung Park, and Hwanjo Yu. "Sequential and Diverse Recommendation with Long Tail." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/380.
Full textZhao, Zhou, Lingtao Meng, Jun Xiao, Min Yang, Fei Wu, Deng Cai, Xiaofei He, and Yueting Zhuang. "Attentional Image Retweet Modeling via Multi-Faceted Ranking Network Learning." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/442.
Full textYe, Mang, Zheng Wang, Xiangyuan Lan, and Pong C. Yuen. "Visible Thermal Person Re-Identification via Dual-Constrained Top-Ranking." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/152.
Full textAlves, Marcos Antonio, Ivan Reinaldo Meneghini, and Frederico Gadelha Guimarães. "Learning Pairwise Comparisons with Machine Learning for Large-Scale Multi-Criteria Decision Making Problems." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2021. http://dx.doi.org/10.21528/cbic2021-13.
Full textSun, Zhu, Jie Yang, Jie Zhang, Alessandro Bozzon, Yu Chen, and Chi Xu. "MRLR: Multi-level Representation Learning for Personalized Ranking in Recommendation." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/391.
Full textReports on the topic "Ranking to Learn"
Oxfam’s “Behind the Brands” Campaign: How a scorecard ranking, corporate engagement, and consumer activism catalyzed the largest food and beverage companies to change their ways. Population Council, 2017. http://dx.doi.org/10.31899/sbsr2017.1001.
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