Filters








4,560 Hits in 3.8 sec

Neural Networks for Information Retrieval

Tom Kenter, Alexey Borisov, Christophe Van Gysel, Mostafa Dehghani, Maarten de Rijke, Bhaskar Mitra
2018 Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining - WSDM '18  
The aim of this full-day tutorial is to give a clear overview of current tried-and-trusted neural methods in IR and how they bene t IR research.  ...  The amount of information available can be overwhelming both for junior students and for experienced researchers looking for new research topics and directions.  ...  Recently, it was shown that recurrent neural networks can learn to account for biases in user clicks directly from the click-through data, i.e., without the need for a prede ned set of rules as is customary  ... 
doi:10.1145/3159652.3162009 dblp:conf/wsdm/KenterBGDRM18 fatcat:ybdeuuxcbnh2np34k3y4ve5ovu

Neural Networks for Information Retrieval

Tom Kenter, Alexey Borisov, Christophe Van Gysel, Mostafa Dehghani, Maarten de Rijke, Bhaskar Mitra
2017 Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '17  
The aim of this full-day tutorial is to give a clear overview of current tried-and-trusted neural methods in IR and how they bene t IR research.  ...  The amount of information available can be overwhelming both for junior students and for experienced researchers looking for new research topics and directions.  ...  Recently, it was shown that recurrent neural networks can learn to account for biases in user clicks directly from the click-through data, i.e., without the need for a prede ned set of rules as is customary  ... 
doi:10.1145/3077136.3082062 dblp:conf/sigir/KenterBGDRM17 fatcat:yxuiajzjlfaixlnhc6rrsud6ry

Neural Networks for Information Retrieval [article]

Tom Kenter, Alexey Borisov, Christophe Van Gysel, Mostafa Dehghani, Maarten de Rijke, Bhaskar Mitra
2017 arXiv   pre-print
The aim of this full-day tutorial is to give a clear overview of current tried-and-trusted neural methods in IR and how they benefit IR research.  ...  The amount of information available can be overwhelming both for junior students and for experienced researchers looking for new research topics and directions.  ...  Recently, it was shown that recurrent neural networks can learn to account for biases in user clicks directly from the click-through data, i.e., without the need for a prede ned set of rules as is customary  ... 
arXiv:1707.04242v1 fatcat:4idscmq26fa5bjupldwuyghq4m

Unbiased Top-k Learning to Rank with Causal Likelihood Decomposition [article]

Haiyuan Zhao, Jun Xu, Xiao Zhang, Guohao Cai, Zhenhua Dong, Ji-Rong Wen
2022 arXiv   pre-print
Unbiased learning to rank has been proposed to alleviate the biases in the search ranking, making it possible to train ranking models with user interaction data.  ...  Advantages of CLD include theoretical soundness and a unified framework for pointwise and pairwise unbiased top-k learning to rank.  ...  Implementation details: Similar to existing studies [1, 4, 40] , we used a three layers neural networks with 𝑒𝑙𝑢 activation function as the ranking model for Naive, IPS, Oracle and CLD pair , with  ... 
arXiv:2204.00815v1 fatcat:vfdx4xdvtfet7cg2ujfrihtg7a

Investigating Weak Supervision in Deep Ranking

Yukun Zheng, Yiqun Liu, Zhen Fan, Cheng Luo, Qingyao Ai, Min Zhang, Shaoping Ma
2019 Data and Information Management  
A number of deep neural networks have been proposed to improve the performance of document ranking in information retrieval studies.  ...  In this work, we adopt two kinds of weakly supervised relevance, BM25-based relevance and click model-based relevance, and make a deep investigation into their differences in the training of neural ranking  ...  In this paper, we used the implementations of these models from RankLib.3 RankNet is a well-known ranking model using a neural network trained with pairwise losses.  ... 
doi:10.2478/dim-2019-0010 fatcat:gjutpp777vdvljqxtf2r6nvuwy

Neural Networks for Information Retrieval [article]

Tom Kenter and Alexey Borisov and Christophe Van Gysel and Mostafa Dehghani and Maarten de Rijke and Bhaskar Mitra
2018 arXiv   pre-print
The aim of this full-day tutorial is to give a clear overview of current tried-and-trusted neural methods in IR and how they benefit IR.  ...  The amount of information available can be overwhelming both for junior students and for experienced researchers looking for new research topics and directions.  ...  Recently, it was shown that recurrent neural networks can learn to account for biases in user clicks directly from click-through, i.e., without the need for a predefined set of rules as is customary for  ... 
arXiv:1801.02178v1 fatcat:c3kevelcrffodift2vvwnoscjq

A General Framework for Counterfactual Learning-to-Rank [article]

Aman Agarwal, Kenta Takatsu, Ivan Zaitsev, Thorsten Joachims
2019 arXiv   pre-print
metrics (e.g., Discounted Cumulative Gain (DCG)) as well as a broad class of models (e.g., deep networks).  ...  Going beyond this special case, this paper provides a general and theoretically rigorous framework for counterfactual learning-to-rank that enables unbiased training for a broad class of additive ranking  ...  The first is SVM PropDCG, which generalizes a Ranking SVM to directly optimize a bound on DCG from biased click data.  ... 
arXiv:1805.00065v3 fatcat:cjvtma4rrjglrmlgckcn7uh5gu

Modeling Relevance Ranking under the Pre-training and Fine-tuning Paradigm [article]

Lin Bo, Liang Pang, Gang Wang, Jun Xu, XiuQiang He, Ji-Rong Wen
2021 arXiv   pre-print
Recently, pre-trained language models such as BERT have been applied to document ranking for information retrieval, which first pre-train a general language model on an unlabeled large corpus and then  ...  More importantly, the pre-trained representations, are fine-tuned together with handcrafted learning-to-rank features under a wide and deep network architecture.  ...  Machine learning models, especially deep neural networks [16] have been applied to relevance ranking and many ranking techniques have been developed [18, 43] .  ... 
arXiv:2108.05652v1 fatcat:hiafpiym2jeqtdsanl52zfnrq4

Personalized News Recommendation: Methods and Challenges [article]

Chuhan Wu, Fangzhao Wu, Yongfeng Huang, Xing Xie
2022 arXiv   pre-print
We first review the techniques for tackling each core problem in a personalized news recommender system and the challenges they face.  ...  Next, we introduce the public datasets and evaluation methods for personalized news recommendation.  ...  There are several methods that use neural networks to learn user representations from users' click behaviors. For example, Okura et al.  ... 
arXiv:2106.08934v3 fatcat:iagqsw73hrehxaxpvpydvtr26m

Modeling and Simultaneously Removing Bias via Adversarial Neural Networks [article]

John Moore, Joel Pfeiffer, Kai Wei, Rishabh Iyer, Denis Charles, Ran Gilad-Bachrach, Levi Boyles, Eren Manavoglu
2018 arXiv   pre-print
In this work, we develop a novel Adversarial Neural Network (ANN) model, an alternative approach which creates a representation of the data that is invariant to the bias.  ...  In real world systems, the predictions of deployed Machine Learned models affect the training data available to build subsequent models.  ...  For comparison, we perform the same evaluations for an ANN with λ = 0. is model can be seen as a complete independent vanilla neural network optimizing over , while a separate Bias network is able to observe  ... 
arXiv:1804.06909v1 fatcat:k3pqn7btvnfkddengxxxym4khu

Incorporating Vision Bias into Click Models for Image-oriented Search Engine [article]

Ningxin Xu, Cheng Yang, Yixin Zhu, Xiaowei Hu, Changhu Wang
2021 arXiv   pre-print
Most typical click models assume that the probability of a document to be examined by users only depends on position, such as PBM and UBM. It works well in various kinds of search engines.  ...  Specifically, we apply this assumption to classical click models and propose an extended model, to better capture the examination probabilities of documents.  ...  These works use complex deep neural networks to build up click models and encode the context attributes by vectors.  ... 
arXiv:2101.02459v1 fatcat:hjqgwgzxvfcjdhdzwyoe6bdvte

Debiasing Neural Retrieval via In-batch Balancing Regularization [article]

Yuantong Li, Xiaokai Wei, Zijian Wang, Shen Wang, Parminder Bhatia, Xiaofei Ma, Andrew Arnold
2022 arXiv   pre-print
People frequently interact with information retrieval (IR) systems, however, IR models exhibit biases and discrimination towards various demographics.  ...  The in-processing fair ranking methods provide a trade-offs between accuracy and fairness through adding a fairness-related regularization term in the loss function.  ...  network.  ... 
arXiv:2205.09240v1 fatcat:75hpf4cuzfaapiggsbbw2jtub4

FairRank: Fairness-aware Single-tower Ranking Framework for News Recommendation [article]

Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang
2022 arXiv   pre-print
In this paper, we propose FairRank, which is a fairness-aware single-tower ranking framework for news recommendation.  ...  However, these models can easily inherit the biases related to users' sensitive attributes (e.g., demographics) encoded in training click data, and may generate recommendation results that are unfair to  ...  which can be formulated as ŷ = 𝑓 (u 𝑐 , h 𝑐 ), where 𝑓 (•) is a relevance function that is often implemented by inner product or feedforward neural networks.  ... 
arXiv:2204.00541v1 fatcat:ludwigtdeffdbfjzxfjl55rbwa

Can Clicks Be Both Labels and Features?

Tao Yang, Chen Luo, Hanqing Lu, Parth Gupta, Bing Yin, Qingyao Ai
2022 Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval  
Using implicit feedback collected from user clicks as training labels for learning-to-rank algorithms is a well-developed paradigm that has been extensively studied and used in modern IR systems.  ...  In this paper, we explore the possibility of incorporating user clicks as both training labels and ranking features for learning to rank.  ...  For LTR models, we implemented the ranking function 𝑓 using feed-forward neural networks with 2 hidden layers (32 neurons per layer).  ... 
doi:10.1145/3477495.3531948 fatcat:6hmvv2qjtvhdda4ydo4j3ai2p4

Scalable Exploration for Neural Online Learning to Rank with Perturbed Feedback

Yiling Jia, Hongning Wang
2022 Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval  
Our solution is based on an ensemble of ranking models trained with perturbed user click feedback.  ...  Driven by the recent developments in optimization and generalization of DNNs, learning a neural ranking model online from its interactions with users becomes possible.  ...  ACKNOWLEDGEMENTS We want to thank the reviewers for their insightful comments.  ... 
doi:10.1145/3477495.3532057 fatcat:vdh4qxuxwzbpncjqquqechsq6y
« Previous Showing results 1 — 15 out of 4,560 results