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Unbiased Learning to Rank in Feeds Recommendation
Web Search and Data Mining, 2021In feeds recommendation, users are able to constantly browse items generated by never-ending feeds using mobile phones. The implicit feedback from users is an important resource for learning to rank, however, building ranking functions from such observed
Xinwei Wu +5 more
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Learning to Rank with Ensemble Ranking SVM
Neural Processing Letters, 2014In this paper, we propose a novel learning to rank method using Ensemble Ranking SVM. Ensemble Ranking SVM is based on Ranking SVM which has been commonly used for learning to rank. The basic idea of Ranking SVM is to formulate the problem of learning to rank as that of binary classification on instance pairs.
Licheng Jiao, Cheolkon Jung, Yanbo Shen
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2017
In an era where accumulating data is easy and storing it inexpensive, feature selection plays a central role in helping to reduce the high-dimensionality of huge amounts of otherwise meaningless data. In this paper, we propose a graph-based method for feature selection that ranks features by identifying the most important ones into arbitrary set of ...
Giorgio Roffo +2 more
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In an era where accumulating data is easy and storing it inexpensive, feature selection plays a central role in helping to reduce the high-dimensionality of huge amounts of otherwise meaningless data. In this paper, we propose a graph-based method for feature selection that ranks features by identifying the most important ones into arbitrary set of ...
Giorgio Roffo +2 more
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Learning to rank with document ranks and scores
Knowledge-Based Systems, 2011The problem of ''Learning to rank'' is a popular research topic in Information Retrieval (IR) and machine learning communities. Some existing list-wise methods, such as AdaRank, directly use the IR measures as performance functions to quantify how well a ranking function can predict rankings.
Changqin Huang +3 more
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Hybrid Learning to Rank for Financial Event Ranking [PDF]
Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021 The financial markets are moved by events such as the issuance of administrative orders. The participants in financial markets (e.g., traders) thus pay constant attention to financial news relevant to the financial asset (e.g., oil) of interest. Due to the large scale of news stream, it is time and labor intensive to manually identify influential ...
Moxin Li +4 more
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International Conference on Information and Knowledge Management, 2020
Traditional Learning to Rank (LTR) models in E-commerce are usually trained on logged data from a single domain. However, data may come from multiple domains, such as hundreds of countries in international E-commerce platforms.
Pengcheng Li +4 more
semanticscholar +1 more source
Traditional Learning to Rank (LTR) models in E-commerce are usually trained on logged data from a single domain. However, data may come from multiple domains, such as hundreds of countries in international E-commerce platforms.
Pengcheng Li +4 more
semanticscholar +1 more source
Proceedings of the ACM International Conference on Image and Video Retrieval, 2010 Social images sharing websites, such as Flickr and Picasa, are becoming very popular nowadays. Users are generally recommended to annotate images with free tags, yet these tags are orderless, and thus quite limited for applications like image search, retrieval and management.
Changshui Zhang +3 more
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Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, 2008
Designing effective ranking functions is a core problem for information retrieval and Web search since the ranking functions directly impact the relevance of the search results. The problem has been the focus of much of the research at the intersection of Web search and machine learning, and learning ranking functions from preference data in particular
Gui-Rong Xue +3 more
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Designing effective ranking functions is a core problem for information retrieval and Web search since the ranking functions directly impact the relevance of the search results. The problem has been the focus of much of the research at the intersection of Web search and machine learning, and learning ranking functions from preference data in particular
Gui-Rong Xue +3 more
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Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, 2007
Collection selection, ranking collections according to user query is crucial in distributed search. However, few features are used to rank collections in the current collection selection methods, while hundreds of features are exploited to rank web pages in web search.
Jingfang Xu, Xing Li
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Collection selection, ranking collections according to user query is crucial in distributed search. However, few features are used to rank collections in the current collection selection methods, while hundreds of features are exploited to rank web pages in web search.
Jingfang Xu, Xing Li
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LiPO: Listwise Preference Optimization through Learning-to-Rank
North American Chapter of the Association for Computational LinguisticsAligning language models (LMs) with curated human feedback is critical to control their behaviors in real-world applications. Several recent policy optimization methods, such as DPO and SLiC, serve as promising alternatives to the traditional ...
Tianqi Liu +11 more
semanticscholar +1 more source