Results 321 to 330 of about 1,080,302 (365)
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Information Retrieval, 2005
New general purpose ranking functions are discovered using genetic programming. The TREC WSJ collection was chosen as a training set. A baseline comparison function was chosen as the best of inner product, probability, cosine, and Okapi BM25. An elitist genetic algorithm with a population size 100 was run 13 times for 100 generations and the best ...
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New general purpose ranking functions are discovered using genetic programming. The TREC WSJ collection was chosen as a training set. A baseline comparison function was chosen as the best of inner product, probability, cosine, and Okapi BM25. An elitist genetic algorithm with a population size 100 was run 13 times for 100 generations and the best ...
openaire +2 more sources
Efficient LLM Scheduling by Learning to Rank
Neural Information Processing SystemsIn Large Language Model (LLM) inference, the output length of an LLM request is typically regarded as not known a priori. Consequently, most LLM serving systems employ a simple First-come-first-serve (FCFS) scheduling strategy, leading to Head-Of-Line ...
Yichao Fu +5 more
semanticscholar +1 more source
Proceedings of the 29th on Hypertext and Social Media, 2018
Software robots, or simply bots, have often been regarded as harmless programs confined within the cyberspace. However, recent events in our society proved that they can have important effects on real life as well. Bots have in fact become one of the key tools for disseminating information through online social networks (OSNs), influencing their ...
Perna, Diego, Tagarelli, Andrea
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Software robots, or simply bots, have often been regarded as harmless programs confined within the cyberspace. However, recent events in our society proved that they can have important effects on real life as well. Bots have in fact become one of the key tools for disseminating information through online social networks (OSNs), influencing their ...
Perna, Diego, Tagarelli, Andrea
openaire +2 more sources
2019
In the last decade food understanding has become a very attractive topic. This has implied the growing demand of Computer Vision algorithms for automatic diet assessment to treat or prevent food related diseases. However, the intrinsic variability of food, makes the research in this field incredibly challenging.
Allegra D. +6 more
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In the last decade food understanding has become a very attractive topic. This has implied the growing demand of Computer Vision algorithms for automatic diet assessment to treat or prevent food related diseases. However, the intrinsic variability of food, makes the research in this field incredibly challenging.
Allegra D. +6 more
openaire +3 more sources
Proceedings of the 19th ACM international conference on Information and knowledge management, 2010
An essential issue in document retrieval is ranking, and the documents are ranked by their expected relevance to a given query. Multiple labels are used to represent different level of relevance for documents to a given query, and the corresponding label values are used to quantify the relevance of the documents.
Yuan Lin +4 more
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An essential issue in document retrieval is ranking, and the documents are ranked by their expected relevance to a given query. Multiple labels are used to represent different level of relevance for documents to a given query, and the corresponding label values are used to quantify the relevance of the documents.
Yuan Lin +4 more
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2008 International Conference on Machine Learning and Cybernetics, 2008
Ranking is the key problem for information retrieval and other text applications. Recently, the ranking methods based on machine learning approaches, called learning to rank, become the focus for researchers and practitioners. The main idea of these methods is to apply the various existing and effective algorithms on machine learning to ranking ...
Cong Wang +3 more
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Ranking is the key problem for information retrieval and other text applications. Recently, the ranking methods based on machine learning approaches, called learning to rank, become the focus for researchers and practitioners. The main idea of these methods is to apply the various existing and effective algorithms on machine learning to ranking ...
Cong Wang +3 more
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ACM Trans. Inf. Syst., 2020
How to obtain an unbiased ranking model by learning to rank with biased user feedback is an important research question for IR. Existing work on unbiased learning to rank (ULTR) can be broadly categorized into two groups—the studies on unbiased learning ...
Qingyao Ai +3 more
semanticscholar +1 more source
How to obtain an unbiased ranking model by learning to rank with biased user feedback is an important research question for IR. Existing work on unbiased learning to rank (ULTR) can be broadly categorized into two groups—the studies on unbiased learning ...
Qingyao Ai +3 more
semanticscholar +1 more source
Learning to lurker rank: an evaluation of learning-to-rank methods for lurking behavior analysis [PDF]
Social Network Analysis and Mining, 2018 While being long researched in social science and computer–human interaction, lurking behaviors in online social networks (OSNs) have been computationally studied only in recent years. Remarkably, determining lurking behaviors has been modeled as an unsupervised, eigenvector-centrality-based ranking problem, and it has been shown that lurkers can ...
Perna, Diego +2 more
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Addressing Trust Bias for Unbiased Learning-to-Rank
The Web Conference, 2019Existing unbiased learning-to-rank models use counterfactual inference, notably Inverse Propensity Scoring (IPS), to learn a ranking function from biased click data.
Aman Agarwal +4 more
semanticscholar +1 more source
Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2017
We present a learning-to-rank approach for resource selection. We develop features for resource ranking and present a training approach that does not require human judgments. Our method is well-suited to environments with a large number of resources such as selective search, is an improvement over the state-of-the-art in resource selection for ...
Jamie Callan, Zhuyun Dai, Yubin Kim
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We present a learning-to-rank approach for resource selection. We develop features for resource ranking and present a training approach that does not require human judgments. Our method is well-suited to environments with a large number of resources such as selective search, is an improvement over the state-of-the-art in resource selection for ...
Jamie Callan, Zhuyun Dai, Yubin Kim
openaire +2 more sources