Journal articles on the topic 'Ranking to Learn'
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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.
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Recommender systems have become indispensable for online services since they alleviate the information overload problem for users. Some work has been proposed to support the personalized recommendation by utilizing collaborative filtering to learn the latent user and item representations from implicit interactions between users and items. However, most of existing methods simplify the implicit frequency feedback to binary values, which make collaborative filtering unable to accurately learn the latent user and item features. Moreover, the traditional collaborating filtering methods generally use the linear functions to model the interactions between latent features. The expressiveness of linear functions may not be sufficient to capture the complex structure of users’ interactions and degrades the performance of those recommender systems. In this paper, we propose a neural personalized ranking model for collaborative filtering with the implicit frequency feedback. The proposed method integrates the ranking-based poisson factor model into the neural networks. Specifically, we firstly develop a ranking-based poisson factor model, which combines the poisson factor model and the Bayesian personalized ranking. This model adopts a pair-wise learning method to learn the rankings of uses’ preferences between items. After that, we propose a neural personalized ranking model on top of the ranking-based poisson factor model, named NRPFM, to capture the complex structure of user-item interactions. NRPFM applies the ranking-based poisson factor model on neural networks, which endows the linear ranking-based poisson factor model with a high level of nonlinearities. Experimental results on two real-world datasets show that our proposed method compares favorably with the state-of-the-art recommendation algorithms.
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Li, 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.
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The task of user ranking in signed networks, aiming to predict potential friends and enemies for each user, has attracted increasing attention in numerous applications. Existing approaches are mainly extended from heuristics of the traditional models in unsigned networks. They suffer from two limitations: (1) mainly focus on global rankings thus cannot provide effective personalized ranking results, and (2) have a relatively unrealistic assumption that each user treats her neighbors’ social strengths indifferently. To address these two issues, we propose a supervised method based on random walk to learn social strengths between each user and her neighbors, in which the random walk more likely visits “potential friends” and less likely visits “potential enemies”. We learn the personalized social strengths by optimizing on a particularly designed loss function oriented on ranking. We further present a fast ranking method based on the local structure among each seed node and a certain set of candidates. It much simplifies the proposed ranking model meanwhile maintains the performance. Experimental results demonstrate the superiority of our approach over the state-of-the-art approaches.
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Dzyuba, 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.
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Pattern mining provides useful tools for exploratory data analysis. Numerous efficient algorithms exist that are able to discover various types of patterns in large datasets. Unfortunately, the problem of identifying patterns that are genuinely interesting to a particular user remains challenging. Current approaches generally require considerable data mining expertise or effort from the data analyst, and hence cannot be used by typical domain experts. To address this, we introduce a generic framework for interactive learning of userspecific pattern ranking functions. The user is only asked to rank small sets of patterns, while a ranking function is inferred from this feedback by preference learning techniques. Moreover, we propose a number of active learning heuristics to minimize the effort required from the user, while ensuring that accurate rankings are obtained. We show how the learned ranking functions can be used to mine new, more interesting patterns. We demonstrate two concrete instances of our framework for two different pattern mining tasks, frequent itemset mining and subgroup discovery. We empirically evaluate the capacity of the algorithm to learn pattern rankings by emulating users. Experiments demonstrate that the system is able to learn accurate rankings, and that the active learning heuristics help reduce the required user effort. Furthermore, using the learned ranking functions as search heuristics allows discovering patterns of higher quality than those in the initial set. This shows that machine learning techniques in general, and active preference learning in particular, are promising building blocks for interactive data mining systems.
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Zeng, 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.
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In visual search systems, it is important to address the issue of how to leverage the rich contextual information in a visual computational model to build more robust visual search systems and to better satisfy the user’s need and intention. In this paper, we introduced a ranking model by understanding the complex relations within product visual and textual information in visual search systems. To understand their complex relations, we focused on using graph-based paradigms to model the relations among product images, product category labels, and product names and descriptions. We developed a unified probabilistic hypergraph ranking algorithm, which, modeling the correlations among product visual features and textual features, extensively enriches the description of the image. We conducted experiments on the proposed ranking algorithm on a dataset collected from a real e-commerce website. The results of our comparison demonstrate that our proposed algorithm extensively improves the retrieval performance over the visual distance based ranking.
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Udupi, 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.
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Digital transformation in the field of education plays a significant role especially when used for analysis of various teaching and learning parameters to predict global ranking index of the universities across the world. Machine learning is a subset of computer science facilitates machine to learn the data using various algorithms and predict the results. This research explores the Quacquarelli Symonds approach for evaluating global university rankings and develop machine learning models for predicting global rankings. The research uses exploratory data analysis for analysing the dataset and then evaluate machine learning algorithms using regression techniques for predicting the global rankings. The research also addresses the future scope towards evaluating machine learning algorithms for predicting outcomes using classification and clustering techniques.
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Gao, 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.
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The quality of ranking determines the success or failure of information retrieval and the goal of ranking is to learn a real-valued ranking function that induces a ranking or ordering over an instance space. We focus on a ranking setting which uses truth function to label each pair of instances and the ranking preferences are given randomly from some distributions on the set of possible undirected edge sets of a graph. The contribution of this paper is the given generalization bounds for such ranking algorithm via strong and weak stability. Such stabilities have lower demand than uniform stability and fit for more real applications.
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Wu, 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.
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Although most list-ranking frameworks are based on multilayer perceptrons (MLP), they still face limitations within the method itself in the field of recommender systems in two respects: (1) MLP suffer from overfitting when dealing with sparse vectors. At the same time, the model itself tends to learn in-depth features of user–item interaction behavior but ignores some low-rank and shallow information present in the matrix. (2) Existing ranking methods cannot effectively deal with the problem of ranking between items with the same rating value and the problem of inconsistent independence in reality. We propose a list ranking framework based on linear and non-linear fusion for recommendation from implicit feedback, named RBLF. First, the model uses dense vectors to represent users and items through one-hot encoding and embedding. Second, to jointly learn shallow and deep user–item interaction, we use the interaction grabbing layer to capture the user–item interaction behavior through dense vectors of users and items. Finally, RBLF uses the Bayesian collaborative ranking to better fit the characteristics of implicit feedback. Eventually, the experiments show that the performance of RBLF obtains a significant improvement.
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Zhang, 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.
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In community-based question answering (CQA) platforms, automatic answer ranking for a given question is critical for finding potentially popular answers in early times. The mainstream approaches learn to generate answer ranking scores based on the matching degree between question and answer representations as well as the influence of respondents. However, they encounter two main limitations: (1) Correlations between answers in the same question are often overlooked. (2) Question and respondent representations are built independently of specific answers before affecting answer representations. To address the limitations, we devise a novel graph-based tri-attention network, namely GTAN, which has two innovations. First, GTAN proposes to construct a graph for each question and learn answer correlations from each graph through graph neural networks (GNNs). Second, based on the representations learned from GNNs, an alternating tri-attention method is developed to alternatively build target-aware respondent representations, answer-specific question representations, and context-aware answer representations by attention computation. GTAN finally integrates the above representations to generate answer ranking scores. Experiments on three real-world CQA datasets demonstrate GTAN significantly outperforms state-of-the-art answer ranking methods, validating the rationality of the network architecture.
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Oosterhuis, 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.
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Ranking systems form the basis for online search engines and recommendation services. They process large collections of items, for instance web pages or e-commerce products, and present the user with a small ordered selection. The goal of a ranking system is to help a user find the items they are looking for with the least amount of effort. Thus the rankings they produce should place the most relevant or preferred items at the top of the ranking. Learning to rank is a field within machine learning that covers methods which optimize ranking systems w.r.t. this goal. Traditional supervised learning to rank methods utilize expert-judgements to evaluate and learn, however, in many situations such judgements are impossible or infeasible to obtain. As a solution, methods have been introduced that perform learning to rank based on user clicks instead. The difficulty with clicks is that they are not only affected by user preferences, but also by what rankings were displayed. Therefore, these methods have to prevent being biased by other factors than user preference. This thesis concerns learning to rank methods based on user clicks and specifically aims to unify the different families of these methods. The first part of the thesis consists of three chapters that look at online learning to rank algorithms which learn by directly interacting with users. Its first chapter considers large scale evaluation and shows existing methods do not guarantee correctness and user experience, we then introduce a novel method that can guarantee both. The second chapter proposes a novel pairwise method for learning from clicks that contrasts with the previous prevalent dueling-bandit methods. Our experiments show that our pairwise method greatly outperforms the dueling-bandit approach. The third chapter further confirms these findings in an extensive experimental comparison, furthermore, we also show that the theory behind the dueling-bandit approach is unsound w.r.t. deterministic ranking systems. The second part of the thesis consists of four chapters that look at counterfactual learning to rank algorithms which learn from historically logged click data. Its first chapter takes the existing approach and makes it applicable to top- k settings where not all items can be displayed at once. It also shows that state-of-the-art supervised learning to rank methods can be applied in the counterfactual scenario. The second chapter introduces a method that combines the robust generalization of feature-based models with the high-performance specialization of tabular models. The third chapter looks at evaluation and introduces a method for finding the optimal logging policy that collects click data in a way that minimizes the variance of estimated ranking metrics. By applying this method during the gathering of clicks, one can turn counterfactual evaluation into online evaluation. The fourth chapter proposes a novel counterfactual estimator that considers the possibility that the logging policy has been updated during the gathering of click data. As a result, it can learn much more efficiently when deployed in an online scenario where interventions can take place. The resulting approach is thus both online and counterfactual, our experimental results show that its performance matches the state-of-the-art in both the online and the counterfactual scenario. As a whole, the second part of this thesis proposes a framework that bridges many gaps between areas of online, counterfactual, and supervised learning to rank. It has taken approaches, previously considered independent, and unified them into a single methodology for widely applicable and effective learning to rank from user clicks. Awarded by: University of Amsterdam, Amsterdam, The Netherlands. Supervised by: Maarten de Rijke. Available at: https://hdl.handle.net/11245.1/8ff3aa38-97fb-4d2a-8127-a29a03af4d5c.
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Farias, 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.
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Probability distributions over rankings are crucial for the modeling and design of a wide range of practical systems. In this work, we pursue a nonparametric approach that seeks to learn a distribution over rankings (aka the ranking model) that is consistent with the observed data and has the sparsest possible support (i.e., the smallest number of rankings with nonzero probability mass). We focus on first-order marginal data, which comprise information on the probability that item i is ranked at position j, for all possible item and position pairs. The observed data may be noisy. Finding the sparsest approximation requires brute force search in the worst case. To address this issue, we restrict our search to, what we dub, the signature family, and show that the sparsest model within the signature family can be found computationally efficiently compared with the brute force approach. We then establish that the signature family provides good approximations to popular ranking model classes, such as the multinomial logit and the exponential family classes, with support size that is small relative to the dimension of the observed data. We test our methods on two data sets: the ranked election data set from the American Psychological Association and the preference ordering data on 10 different sushi varieties.
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Kim, Chris, Xiao Lin, Christopher Collins, Graham W. Taylor, and Mohamed R. Amer. "Learn, Generate, Rank, Explain: A Case Study of Visual Explanation by Generative Machine Learning." ACM Transactions on Interactive Intelligent Systems 11, no. 3-4 (December 31, 2021): 1–34. http://dx.doi.org/10.1145/3465407.
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While the computer vision problem of searching for activities in videos is usually addressed by using discriminative models, their decisions tend to be opaque and difficult for people to understand. We propose a case study of a novel machine learning approach for generative searching and ranking of motion capture activities with visual explanation. Instead of directly ranking videos in the database given a text query, our approach uses a variant of Generative Adversarial Networks (GANs) to generate exemplars based on the query and uses them to search for the activity of interest in a large database. Our model is able to achieve comparable results to its discriminative counterpart, while being able to dynamically generate visual explanations. In addition to our searching and ranking method, we present an explanation interface that enables the user to successfully explore the model’s explanations and its confidence by revealing query-based, model-generated motion capture clips that contributed to the model’s decision. Finally, we conducted a user study with 44 participants to show that by using our model and interface, participants benefit from a deeper understanding of the model’s conceptualization of the search query. We discovered that the XAI system yielded a comparable level of efficiency, accuracy, and user-machine synchronization as its black-box counterpart, if the user exhibited a high level of trust for AI explanation.
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Guo, Yuchen, Guiguang Ding, Jungong Han, Xiaohan Ding, Sicheng Zhao, Zheng Wang, Chenggang Yan, and Qionghai Dai. "Dual-View Ranking with Hardness Assessment for Zero-Shot Learning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8360–67. http://dx.doi.org/10.1609/aaai.v33i01.33018360.
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Zero-shot learning (ZSL) is to build recognition models for previously unseen target classes which have no labeled data for training by transferring knowledge from some other related auxiliary source classes with abundant labeled samples to the target ones with class attributes as the bridge. The key is to learn a similarity based ranking function between samples and class labels using the labeled source classes so that the proper (unseen) class label for a test sample can be identified by the function. In order to learn the function, single-view ranking based loss is widely used which aims to rank the true label prior to the other labels for a training sample. However, we argue that the ranking can be performed from the other view, which aims to place the images belonging to a label before the images from the other classes. Motivated by it, we propose a novel DuAl-view RanKing (DARK) loss for zeroshot learning simultaneously ranking labels for an image by point-to-point metric and ranking images for a label by pointto-set metric, which is capable of better modeling the relationship between images and classes. In addition, we also notice that previous ZSL approaches mostly fail to well exploit the hardness of training samples, either using only very hard ones or using all samples indiscriminately. In this work, we also introduce a sample hardness assessment method to ZSL which assigns different weights to training samples based on their hardness, which leads to a more accurate and robust ZSL model. Experiments on benchmarks demonstrate that DARK outperforms the state-of-the-arts for (generalized) ZSL.
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Eleftheriou, Konstantinos, and Michael Polemis. "One list to fit them all: What do we learn from journal ranking?" Finance Research Letters 35 (July 2020): 101278. http://dx.doi.org/10.1016/j.frl.2019.08.026.
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Xiao, Teng, and Suhang Wang. "Towards Off-Policy Learning for Ranking Policies with Logged Feedback." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (June 28, 2022): 8700–8707. http://dx.doi.org/10.1609/aaai.v36i8.20849.
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Probabilistic learning to rank (LTR) has been the dominating approach for optimizing the ranking metric, but cannot maximize long-term rewards. Reinforcement learning models have been proposed to maximize user long-term rewards by formulating the recommendation as a sequential decision-making problem, but could only achieve inferior accuracy compared to LTR counterparts, primarily due to the lack of online interactions and the characteristics of ranking. In this paper, we propose a new off-policy value ranking (VR) algorithm that can simultaneously maximize user long-term rewards and optimize the ranking metric offline for improved sample efficiency in a unified Expectation-Maximization (EM) framework. We theoretically and empirically show that the EM process guides the leaned policy to enjoy the benefit of integration of the future reward and ranking metric, and learn without any online interactions. Extensive offline and online experiments demonstrate the effectiveness of our methods
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Li, Fei, Jia Jia Huang, Min Peng, and Rui Cai. "Feedback Ranking Method in Topic-Based Retrieval." Applied Mechanics and Materials 339 (July 2013): 269–74. http://dx.doi.org/10.4028/www.scientific.net/amm.339.269.
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Ranking has an extensive application in analyzing public opinions of social network (SN), such as searching the most hot topic or the most relevant articles that the user concerning. In these scenarios, due to the different requirements of users, there is need to rank the object set from different aspects and to re-rank the object set by integrating these different results to acquire a synthesize rank result.In this paper, we proposed a novel Feedback Ranking method, which lets two basic rankers learn from each other during the mutual process by providing each one's result as feedback to the other so as to boost the ranking performance. During the mutual ranking refinement process, we utilize iSRCC---an improvement on Spearman Rank Correlation to calculate the weight of each basic rankers dynamically. We apply this method into the article ranking problem on topic-query retrieval and evaluate its effectiveness on the TAC09 data set. Overall evaluation results are promising.
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Yao, Jing, Zhicheng Dou, Jun Xu, and Ji-Rong Wen. "RLPS: A Reinforcement Learning–Based Framework for Personalized Search." ACM Transactions on Information Systems 39, no. 3 (May 6, 2021): 1–29. http://dx.doi.org/10.1145/3446617.
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Personalized search is a promising way to improve search qualities by taking user interests into consideration. Recently, machine learning and deep learning techniques have been successfully applied to search result personalization. Most existing models simply regard the personal search history as a static set of user behaviors and learn fixed ranking strategies based on all the recorded data. Though improvements have been achieved, the essence that the search process is a sequence of interactions between the search engine and user is ignored. The user’s interests may dynamically change during the search process, therefore, it would be more helpful if a personalized search model could track the whole interaction process and adjust its ranking strategy continuously. In this article, we adapt reinforcement learning to personalized search and propose a framework, referred to as RLPS. It utilizes a Markov Decision Process ( MDP ) to track sequential interactions between the user and search engine, and continuously update the underlying personalized ranking model with the user’s real-time feedback to learn the user’s dynamic interests. Within this framework, we implement two models: the listwise RLPS-L and the hierarchical RLPS-H. RLPS-L interacts with users and trains the ranking model with document lists, while RLPS-H improves model training by designing a layered structure and introducing document pairs. In addition, we also design a feedback-aware personalized ranking component to capture the user’s feedback, which impacts the user interest profile for the next query. Significant improvements over existing personalized search models are observed in the experiments on the public AOL search log and a commercial log.
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Werner, Tino. "A review on instance ranking problems in statistical learning." Machine Learning 111, no. 2 (November 18, 2021): 415–63. http://dx.doi.org/10.1007/s10994-021-06122-3.
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AbstractRanking problems, also known as preference learning problems, define a widely spread class of statistical learning problems with many applications, including fraud detection, document ranking, medicine, chemistry, credit risk screening, image ranking or media memorability. While there already exist reviews concentrating on specific types of ranking problems like label and object ranking problems, there does not yet seem to exist an overview concentrating on instance ranking problems that both includes developments in distinguishing between different types of instance ranking problems as well as careful discussions about their differences and the applicability of the existing ranking algorithms to them. In instance ranking, one explicitly takes the responses into account with the goal to infer a scoring function which directly maps feature vectors to real-valued ranking scores, in contrast to object ranking problems where the ranks are given as preference information with the goal to learn a permutation. In this article, we systematically review different types of instance ranking problems and the corresponding loss functions resp. goodness criteria. We discuss the difficulties when trying to optimize those criteria. As for a detailed and comprehensive overview of existing machine learning techniques to solve such ranking problems, we systematize existing techniques and recapitulate the corresponding optimization problems in a unified notation. We also discuss to which of the instance ranking problems the respective algorithms are tailored and identify their strengths and limitations. Computational aspects and open research problems are also considered.
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Wu, Yuehong, Bowen Lu, Lin Tian, and Shangsong Liang. "Learning to Co-Embed Queries and Documents." Electronics 11, no. 22 (November 11, 2022): 3694. http://dx.doi.org/10.3390/electronics11223694.
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Learning to Rank (L2R) methods that utilize machine learning techniques to solve the ranking problems have been widely studied in the field of information retrieval. Existing methods usually concatenate query and document features as training input, without explicit understanding of relevance between queries and documents, especially in pairwise based ranking approach. Thus, it is an interesting question whether we can devise an algorithm that effectively describes the relation between queries and documents to learn a better ranking model without incurring huge parameter costs. In this paper, we present a Gaussian Embedding model for Ranking (GERank), an architecture for co-embedding queries and documents, such that each query or document is represented by a Gaussian distribution with mean and variance. Our GERank optimizes an energy-based loss based on the pairwise ranking framework. Additionally, the KL-divergence is utilized to measure the relevance between queries and documents. Experimental results on two LETOR datasets and one TREC dataset demonstrate that our model obtains a remarkable improvement in the ranking performance compared with the state-of-the-art retrieval models.
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Lai, Songxuan, Lianwen Jin, Luojun Lin, Yecheng Zhu, and Huiyun Mao. "SynSig2Vec: Learning Representations from Synthetic Dynamic Signatures for Real-World Verification." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 735–42. http://dx.doi.org/10.1609/aaai.v34i01.5416.
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An open research problem in automatic signature verification is the skilled forgery attacks. However, the skilled forgeries are very difficult to acquire for representation learning. To tackle this issue, this paper proposes to learn dynamic signature representations through ranking synthesized signatures. First, a neuromotor inspired signature synthesis method is proposed to synthesize signatures with different distortion levels for any template signature. Then, given the templates, we construct a lightweight one-dimensional convolutional network to learn to rank the synthesized samples, and directly optimize the average precision of the ranking to exploit relative and fine-grained signature similarities. Finally, after training, fixed-length representations can be extracted from dynamic signatures of variable lengths for verification. One highlight of our method is that it requires neither skilled nor random forgeries for training, yet it surpasses the state-of-the-art by a large margin on two public benchmarks.
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Shen, Song-Qing, Bin-Bin Yang, and Wei Gao. "AUC Optimization with a Reject Option." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5684–91. http://dx.doi.org/10.1609/aaai.v34i04.6023.
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Making an erroneous decision may cause serious results in diverse mission-critical tasks such as medical diagnosis and bioinformatics. Previous work focuses on classification with a reject option, i.e., abstain rather than classify an instance of low confidence. Most mission-critical tasks are always accompanied with class imbalance and cost sensitivity, where AUC has been shown a preferable measure than accuracy in classification. In this work, we propose the framework of AUC optimization with a reject option, and the basic idea is to withhold the decision of ranking a pair of positive and negative instances with a lower cost, rather than mis-ranking. We obtain the Bayes optimal solution for ranking, and learn the reject function and score function for ranking, simultaneously. An online algorithm has been developed for AUC optimization with a reject option, by considering the convex relaxation and plug-in rule. We verify, both theoretically and empirically, the effectiveness of the proposed algorithm.
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Alcaide, María Ángeles, Elena De La Poza, and Natividad Guadalajara. "Assessing the Sustainability of High-Value Brands in the IT Sector." Sustainability 11, no. 6 (March 15, 2019): 1598. http://dx.doi.org/10.3390/su11061598.
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Nowadays, companies have more freedom on how they can report their corporate social responsibility (CSR) actions and outcomes, despite them being increasingly important for how investors and shareholders can obtain knowledge about companies’ non-financial aspects. This is why more importance is being attached to sustainability rankings as an additional tool to seek excellence and distinguish between companies. The main objective of the present research was to analyze the degree of similarity in sustainability valuations among the most important open-access sustainability rankings that have appeared in the last decade (Green Ranking, RepTrack, Global 100 most sustainable corporations, and Finance Yahoo Sustainability). The secondary objective was to study whether these rankings incorporated the most de facto prestigious brands, and the third objective was to learn of the influence of the level of controversy in Finance Yahoo Sustainability scores in technological companies. Our results reveal wide variability among open-access CSR rankings. Not all the most valued brands appear in the sustainability rankings, which indicates the differences between the rankings of brands and CSR rankings. Finally, the level of controversy was found to be an important aspect in companies’ CSR scores.
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Fu, Zheren, Yan Li, Zhendong Mao, Quan Wang, and Yongdong Zhang. "Deep Metric Learning with Self-Supervised Ranking." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (May 18, 2021): 1370–78. http://dx.doi.org/10.1609/aaai.v35i2.16226.
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Deep metric learning aims to learn a deep embedding space, where similar objects are pushed towards together and different objects are repelled against. Existing approaches typically use inter-class characteristics, e.g. class-level information or instance-level similarity, to obtain semantic relevance of data points and get a large margin between different classes in the embedding space. However, the intra-class characteristics, e.g. local manifold structure or relative relationship within the same class, are usually overlooked in the learning process. Hence the data structure cannot be fully exploited and the output embeddings have limitation in retrieval. More importantly, retrieval results lack in a good ranking. This paper presents a novel self-supervised ranking auxiliary framework, which captures intra-class characteristics as well as inter-class characteristics for better metric learning. Our method defines specific transform functions to simulates the local structure change of intra-class in the initial image domain, and formulates a self-supervised learning procedure to fully exploit this property and preserve it in the embedding space. Extensive experiments on three standard benchmarks show that our method significantly improves and outperforms the state-of-the-art methods on the performances of both retrieval and ranking by 2%-4%.
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Rao, Jinfeng, Wei Yang, Yuhao Zhang, Ferhan Ture, and Jimmy Lin. "Multi-Perspective Relevance Matching with Hierarchical ConvNets for Social Media Search." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 232–40. http://dx.doi.org/10.1609/aaai.v33i01.3301232.
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Despite substantial interest in applications of neural networks to information retrieval, neural ranking models have mostly been applied to “standard” ad hoc retrieval tasks over web pages and newswire articles. This paper proposes MP-HCNN (Multi-Perspective Hierarchical Convolutional Neural Network), a novel neural ranking model specifically designed for ranking short social media posts. We identify document length, informal language, and heterogeneous relevance signals as features that distinguish documents in our domain, and present a model specifically designed with these characteristics in mind. Our model uses hierarchical convolutional layers to learn latent semantic soft-match relevance signals at the character, word, and phrase levels. A poolingbased similarity measurement layer integrates evidence from multiple types of matches between the query, the social media post, as well as URLs contained in the post. Extensive experiments using Twitter data from the TREC Microblog Tracks 2011–2014 show that our model significantly outperforms prior feature-based as well as existing neural ranking models. To our best knowledge, this paper presents the first substantial work tackling search over social media posts using neural ranking models. Our code and data are publicly available.1
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Sigmawaty, Dinda, and Mirna Adriani. "LEARNING WORD RELATEDNESS OVER TIME FOR TEMPORAL RANKING." Jurnal Ilmu Komputer dan Informasi 12, no. 2 (July 8, 2019): 91. http://dx.doi.org/10.21609/jiki.v12i2.745.
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Queries and ranking with temporal aspects gain significant attention in field of Information Retrieval. While searching for articles published over time, the relevant documents usually occur in certain temporal patterns. Given a query that is implicitly time sensitive, we develop a temporal ranking using the important times of query by drawing from the distribution of query trend relatedness over time. We also combine the model with Dual Embedding Space Model (DESM) in the temporal model according to document timestamp. We apply our model using three temporal word embeddings algorithms to learn relatedness of words from news archive in Bahasa Indonesia: (1) QT-W2V-Rank using Word2Vec (2) QT-OW2V-Rank using OrthoTrans-Word2Vec (3) QT-DBE-Rank using Dynamic Bernoulli Embeddings. The highest score was achieved with static word embeddings learned separately over time, called QT-W2V-Rank, which is 66% in average precision and 68% in early precision. Furthermore, studies of different characteristics of temporal topics showed that QT-W2V-Rank is also more effective in capturing temporal patterns such as spikes, periodicity, and seasonality than the baselines.
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Zhu, Lin, Yihong Chen, and Bowen He. "A Domain Generalization Perspective on Listwise Context Modeling." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5965–72. http://dx.doi.org/10.1609/aaai.v33i01.33015965.
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As one of the most popular techniques for solving the ranking problem in information retrieval, Learning-to-rank (LETOR) has received a lot of attention both in academia and industry due to its importance in a wide variety of data mining applications. However, most of existing LETOR approaches choose to learn a single global ranking function to handle all queries, and ignore the substantial differences that exist between queries. In this paper, we propose a domain generalization strategy to tackle this problem. We propose QueryInvariant Listwise Context Modeling (QILCM), a novel neural architecture which eliminates the detrimental influence of inter-query variability by learning query-invariant latent representations, such that the ranking system could generalize better to unseen queries. We evaluate our techniques on benchmark datasets, demonstrating that QILCM outperforms previous state-of-the-art approaches by a substantial margin.
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Singh, Vikram, and Ajay Singh. "Learn-As-You-Go : Feedback-Driven Result Ranking and Query Refinement for Interactive Data Exploration." Procedia Computer Science 125 (2018): 550–59. http://dx.doi.org/10.1016/j.procs.2017.12.071.
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Zhang, Xiao, Meng Liu, Jianhua Yin, Zhaochun Ren, and Liqiang Nie. "Question Tagging via Graph-guided Ranking." ACM Transactions on Information Systems 40, no. 1 (January 31, 2022): 1–23. http://dx.doi.org/10.1145/3468270.
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With the increasing prevalence of portable devices and the popularity of community Question Answering (cQA) sites, users can seamlessly post and answer many questions. To effectively organize the information for precise recommendation and easy searching, these platforms require users to select topics for their raised questions. However, due to the limited experience, certain users fail to select appropriate topics for their questions. Thereby, automatic question tagging becomes an urgent and vital problem for the cQA sites, yet it is non-trivial due to the following challenges. On the one hand, vast and meaningful topics are available yet not utilized in the cQA sites; how to model and tag them to relevant questions is a highly challenging problem. On the other hand, related topics in the cQA sites may be organized into a directed acyclic graph. In light of this, how to exploit relations among topics to enhance their representations is critical. To settle these challenges, we devise a graph-guided topic ranking model to tag questions in the cQA sites appropriately. In particular, we first design a topic information fusion module to learn the topic representation by jointly considering the name and description of the topic. Afterwards, regarding the special structure of topics, we propose an information propagation module to enhance the topic representation. As the comprehension of questions plays a vital role in question tagging, we design a multi-level context-modeling-based question encoder to obtain the enhanced question representation. Moreover, we introduce an interaction module to extract topic-aware question information and capture the interactive information between questions and topics. Finally, we utilize the interactive information to estimate the ranking scores for topics. Extensive experiments on three Chinese cQA datasets have demonstrated that our proposed model outperforms several state-of-the-art competitors.
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Chen, Jin, Defu Lian, and Kai Zheng. "Improving One-Class Collaborative Filtering via Ranking-Based Implicit Regularizer." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 37–44. http://dx.doi.org/10.1609/aaai.v33i01.330137.
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One-class collaborative filtering (OCCF) problems are vital in many applications of recommender systems, such as news and music recommendation, but suffers from sparsity issues and lacks negative examples. To address this problem, the state-of-the-arts assigned smaller weights to unobserved samples and performed low-rank approximation. However, the ground-truth ratings of unobserved samples are usually set to zero but ill-defined. In this paper, we propose a ranking-based implicit regularizer and provide a new general framework for OCCF, to avert the ground-truth ratings of unobserved samples. We then exploit it to regularize a ranking-based loss function and design efficient optimization algorithms to learn model parameters. Finally, we evaluate them on three realworld datasets. The results show that the proposed regularizer significantly improves ranking-based algorithms and that the proposed framework outperforms the state-of-the-art OCCF algorithms.
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Simpson, Edwin, Yang Gao, and Iryna Gurevych. "Interactive Text Ranking with Bayesian Optimization: A Case Study on Community QA and Summarization." Transactions of the Association for Computational Linguistics 8 (December 2020): 759–75. http://dx.doi.org/10.1162/tacl_a_00344.
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For many NLP applications, such as question answering and summarization, the goal is to select the best solution from a large space of candidates to meet a particular user’s needs. To address the lack of user or task-specific training data, we propose an interactive text ranking approach that actively selects pairs of candidates, from which the user selects the best. Unlike previous strategies, which attempt to learn a ranking across the whole candidate space, our method uses Bayesian optimization to focus the user’s labeling effort on high quality candidates and integrate prior knowledge to cope better with small data scenarios. We apply our method to community question answering (cQA) and extractive multidocument summarization, finding that it significantly outperforms existing interactive approaches. We also show that the ranking function learned by our method is an effective reward function for reinforcement learning, which improves the state of the art for interactive summarization.
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JIANG, LIANGXIAO, DIANHONG WANG, HARRY ZHANG, ZHIHUA CAI, and BO HUANG. "USING INSTANCE CLONING TO IMPROVE NAIVE BAYES FOR RANKING." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 06 (September 2008): 1121–40. http://dx.doi.org/10.1142/s0218001408006703.
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Improving naive Bayes (simply NB)15,28 for classification has received significant attention. Related work can be broadly divided into two approaches: eager learning and lazy learning.1 Different from eager learning, the key idea for extending naive Bayes using lazy learning is to learn an improved naive Bayes for each test instance. In recent years, several lazy extensions of naive Bayes have been proposed. For example, LBR,30 SNNB,27 and LWNB.8 All these algorithms aim to improve naive Bayes' classification performance. Indeed, they achieve significant improvement in terms of classification, measured by accuracy. In many real-world data mining applications, however, an accurate ranking is more desirable than an accurate classification. Thus a natural question is whether they also achieve significant improvement in terms of ranking, measured by AUC (the area under the ROC curve).2,11,17 Responding to this question, we conduct experiments on the 36 UCI data sets18 selected by Weka12 to investigate their ranking performance and find that they do not significantly improve the ranking performance of naive Bayes. Aiming at scaling up naive Bayes' ranking performance, we present a novel lazy method ICNB (instance cloned naive Bayes) and develop three ICNB algorithms using different instance cloning strategies. We empirically compare them with naive Bayes. The experimental results show that our algorithms achieve significant improvement in terms of AUC. Our research provides a simple but effective method for the applications where an accurate ranking is desirable.
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Zhang, Chuxu, Julia Kiseleva, Sujay Kumar Jauhar, and Ryen W. White. "Grounded Task Prioritization with Context-Aware Sequential Ranking." ACM Transactions on Information Systems 40, no. 4 (October 31, 2022): 1–28. http://dx.doi.org/10.1145/3486861.
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People rely on task management applications and digital assistants to capture and track their tasks, and help with executing them. The burden of organizing and scheduling time for tasks continues to reside with users of these systems, despite the high cognitive load associated with these activities. Users stand to benefit greatly from a task management system capable of prioritizing their pending tasks, thus saving them time and effort. In this article, we make three main contributions. First, we propose the problem of task prioritization, formulating it as a ranking over a user’s pending tasks given a history of previous interactions with a task management system. Second, we perform an extensive analysis on the large-scale anonymized, de-identified logs of a popular task management application, deriving a dataset of grounded, real-world tasks from which to learn and evaluate our proposed system. We also identify patterns in how people record tasks as complete, which vary consistently with the nature of the task. Third, we propose a novel contextual deep learning solution capable of performing personalized task prioritization. In a battery of tests, we show that this approach outperforms several operational baselines and other sequential ranking models from previous work. Our findings have implications for understanding the ways people prioritize and manage tasks with digital tools, and in the design of support for users of task management applications.
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Yan, Yan, Bo-Wen Zhang, Xu-Feng Li, and Zhenhan Liu. "List-wise learning to rank biomedical question-answer pairs with deep ranking recursive autoencoders." PLOS ONE 15, no. 11 (November 9, 2020): e0242061. http://dx.doi.org/10.1371/journal.pone.0242061.
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Biomedical question answering (QA) represents a growing concern among industry and academia due to the crucial impact of biomedical information. When mapping and ranking candidate snippet answers within relevant literature, current QA systems typically refer to information retrieval (IR) techniques: specifically, query processing approaches and ranking models. However, these IR-based approaches are insufficient to consider both syntactic and semantic relatedness and thus cannot formulate accurate natural language answers. Recently, deep learning approaches have become well-known for learning optimal semantic feature representations in natural language processing tasks. In this paper, we present a deep ranking recursive autoencoders (rankingRAE) architecture for ranking question-candidate snippet answer pairs (Q-S) to obtain the most relevant candidate answers for biomedical questions extracted from the potentially relevant documents. In particular, we convert the task of ranking candidate answers to several simultaneous binary classification tasks for determining whether a question and a candidate answer are relevant. The compositional words and their random initialized vectors of concatenated Q-S pairs are fed into recursive autoencoders to learn the optimal semantic representations in an unsupervised way, and their semantic relatedness is classified through supervised learning. Unlike several existing methods to directly choose the top-K candidates with highest probabilities, we take the influence of different ranking results into consideration. Consequently, we define a listwise “ranking error” for loss function computation to penalize inappropriate answer ranking for each question and to eliminate their influence. The proposed architecture is evaluated with respect to the BioASQ 2013-2018 Six-year Biomedical Question Answering benchmarks. Compared with classical IR models, other deep representation models, as well as some state-of-the-art systems for these tasks, the experimental results demonstrate the robustness and effectiveness of rankingRAE.
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Yao, Nan, Feng Qian, and Zuo Lei Sun. "Feature Dimension Reduction and Graph Based Ranking Based Image Classification." Applied Mechanics and Materials 380-384 (August 2013): 4035–38. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.4035.
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Dimensionality reduction (DR) of image features plays an important role in image retrieval and classification tasks. Recently, two types of methods have been proposed to improve both the accuracy and efficiency for the dimensionality reduction problem. One uses Non-negative matrix factorization (NMF) to describe the image distribution on the space of base matrix. Another one for dimension reduction trains a subspace projection matrix to project original data space into some low-dimensional subspaces which have deep architecture, so that the low-dimensional codes would be learned. At the same time, the graph based similarity learning algorithm which tries to exploit contextual information for improving the effectiveness of image rankings is also proposed for image class and retrieval problem. In this paper, after above two methods mentioned are utilized to reduce the high-dimensional features of images respectively, we learn the graph based similarity for the image classification problem. This paper compares the proposed approach with other approaches on an image database.
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Jayasekara, Sivakolundu, Hithanadura Dassanayake, and Anil Fernando. "A Novel Image Retrieval System Based on Histogram Factorization and Contextual Similarity Learning." Applied Mechanics and Materials 380-384 (August 2013): 4148–51. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.4148.
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Image retrieval has been a top topic in the field of both computer vision and machine learning for a long time. Content based image retrieval, which tries to retrieve images from a database visually similar to a query image, has attracted much attention. Two most important issues of image retrieval are the representation and ranking of the images. Recently, bag-of-words based method has shown its power as a representation method. Moreover, nonnegative matrix factorization is also a popular way to represent the data samples. In addition, contextual similarity learning has also been studied and proven to be an effective method for the ranking problem. However, these technologies have never been used together. In this paper, we developed an effective image retrieval system by representing each image using the bag-of-words method as histograms, and then apply the nonnegative matrix factorization to factorize the histograms, and finally learn the ranking score using the contextual similarity learning method. The proposed novel system is evaluated on a large scale image database and the effectiveness is shown.
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Maurya, Sunil Kumar, Xin Liu, and Tsuyoshi Murata. "Graph Neural Networks for Fast Node Ranking Approximation." ACM Transactions on Knowledge Discovery from Data 15, no. 5 (June 26, 2021): 1–32. http://dx.doi.org/10.1145/3446217.
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Graphs arise naturally in numerous situations, including social graphs, transportation graphs, web graphs, protein graphs, etc. One of the important problems in these settings is to identify which nodes are important in the graph and how they affect the graph structure as a whole. Betweenness centrality and closeness centrality are two commonly used node ranking measures to find out influential nodes in the graphs in terms of information spread and connectivity. Both of these are considered as shortest path based measures as the calculations require the assumption that the information flows between the nodes via the shortest paths. However, exact calculations of these centrality measures are computationally expensive and prohibitive, especially for large graphs. Although researchers have proposed approximation methods, they are either less efficient or suboptimal or both. We propose the first graph neural network (GNN) based model to approximate betweenness and closeness centrality. In GNN, each node aggregates features of the nodes in multihop neighborhood. We use this feature aggregation scheme to model paths and learn how many nodes are reachable to a specific node. We demonstrate that our approach significantly outperforms current techniques while taking less amount of time through extensive experiments on a series of synthetic and real-world datasets. A benefit of our approach is that the model is inductive, which means it can be trained on one set of graphs and evaluated on another set of graphs with varying structures. Thus, the model is useful for both static graphs and dynamic graphs. Source code is available at https://github.com/sunilkmaurya/GNN_Ranking
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Thompson, Emmanuel, and Ahmad Mahmoud Talafha. "Regularization-Based Bootstrap Ranking Model: Identifying Healthcare Indicators Among All Level Income Economies." Afrika Statistika 15, no. 3 (June 1, 2020): 2431–49. http://dx.doi.org/10.16929/as/2020.2431.167.
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This study considers the problem of uncertainty of concurrent variables selection among a potential set of healthcare expenditure predictors. It evaluates two regularization (shrinkage) methods: Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net (ENET). To improve the accuracy of identifying important and relevant predictors of healthcare cost, the present study proposes a new methodology in the form of a bootstrapped-regularized regression with percentile rankings. A simulation study under various scenarios was implemented to learn the performance of the proposed methodology. The proposed methodology was applied to healthcare expenditure data for all level income economies: lower-income, lower-middle-income, upper-middle-income, and high-income.
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Wei, Chunting, Jiwei Qin, and Qiulin Ren. "A Ranking Recommendation Algorithm Based on Dynamic User Preference." Sensors 22, no. 22 (November 10, 2022): 8683. http://dx.doi.org/10.3390/s22228683.
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In recent years, hybrid recommendation techniques based on feature fusion have gained extensive attention in the field of list ranking. Most of them fuse linear and nonlinear models to simultaneously learn the linear and nonlinear features of entities and jointly fit user-item interactions. These methods are based on implicit feedback, which can reduce the difficulty of data collection and the time of data preprocessing, but will lead to the lack of entity interaction depth information due to the lack of user satisfaction. This is equivalent to artificially reducing the entity interaction features, limiting the overall performance of the model. To address this problem, we propose a two-stage recommendation model named A-DNR, short for Attention-based Deep Neural Ranking. In the first stage, user short-term preferences are modeled through an attention mechanism network. Then the user short-term preferences and user long-term preferences are fused into dynamic user preferences. In the second stage, the high-order and low-order feature interactions are modeled by a matrix factorization (MF) model and a multi-layer perceptron (MLP) model, respectively. Then, the features are fused through a fully connected layer, and the vectors are mapped to scores. Finally, a ranking list is output through the scores. Experiments on three real-world datasets (Movielens100K, Movielens1M and Yahoo Movies) show that our proposed model achieves significant improvements compared to existing methods.
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Nelken, David. "From pains-taking to pains-giving comparisons." International Journal of Law in Context 12, no. 4 (November 9, 2016): 390–403. http://dx.doi.org/10.1017/s1744552316000161.
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AbstractThis paper distinguishes three ideal-type contexts in which comparisons are used: comparison as a contribution to disciplinary enquiry, as part of deliberately trying to learn lessons and as an essential element of a new form of governmentality concerned with ranking places in terms of social indicators. After offering examples of the way comparisons are employed (and criticised) in each of these exercises, the paper ends by discussing the overlap and feedback between them.
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Akoglu, Leman. "Quantifying Political Polarity Based on Bipartite Opinion Networks." Proceedings of the International AAAI Conference on Web and Social Media 8, no. 1 (May 16, 2014): 2–11. http://dx.doi.org/10.1609/icwsm.v8i1.14524.
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Political inclinations of individuals (liberal vs. conservative) largely shape their opinions on several issues such as abortion, gun control, nuclear power, etc. These opinions are openly exerted inonline forums, news sites, the parliament, and so on. In this paper, we address the problem of quantifying political polarity of individuals and of political issues for classification and ranking. We use signed bipartite networks to represent the opinions of individuals on issues, and formulate the problem as a node classification task. We propose a linear algorithm that exploits network effects to learn both the polarity labels as well as the rankings of people and issues in a completely unsupervised manner. Through extensive experiments we demonstrate that our proposed method provides an effective, fast, and easy-to-implement solution, while outperforming three existing baseline algorithms adapted to signed networks, on real political forum and US Congress datasets.Experiments on a wide variety of synthetic graphs with varying polarity and degree distributions of the nodes further demonstrate the robustness of our approach.
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Dannals, Jennifer E., Emily S. Reit, and Dale T. Miller. "From whom do we learn group norms? Low-ranking group members are perceived as the best sources." Organizational Behavior and Human Decision Processes 161 (November 2020): 213–27. http://dx.doi.org/10.1016/j.obhdp.2020.08.002.
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Sasaki, Takao, and Stephen C. Pratt. "Ants learn to rely on more informative attributes during decision-making." Biology Letters 9, no. 6 (December 23, 2013): 20130667. http://dx.doi.org/10.1098/rsbl.2013.0667.
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Evolutionary theory predicts that animals act to maximize their fitness when choosing among a set of options, such as what to eat or where to live. Making the best choice is challenging when options vary in multiple attributes, and animals have evolved a variety of heuristics to simplify the task. Many of these involve ranking or weighting attributes according to their importance. Because the importance of attributes can vary across time and place, animals might benefit by adjusting weights accordingly. Here, we show that colonies of the ant Temnothorax rugatulus use their experience during nest site selection to increase weights on more informative nest attributes. These ants choose their rock crevice nests on the basis of multiple features. After exposure to an environment where only one attribute differentiated options, colonies increased their reliance on this attribute relative to a second attribute. Although many species show experience-based changes in selectivity based on a single feature, this is the first evidence in animals for adaptive changes in the weighting of multiple attributes. These results show that animal collectives, like individuals, change decision-making strategies according to experience. We discuss how these colony-level changes might emerge from individual behaviour.
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Rodrigo, Enrique G., Juan C. Alfaro, Juan A. Aledo, and José A. Gámez. "Mixture-Based Probabilistic Graphical Models for the Label Ranking Problem." Entropy 23, no. 4 (March 31, 2021): 420. http://dx.doi.org/10.3390/e23040420.
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The goal of the Label Ranking (LR) problem is to learn preference models that predict the preferred ranking of class labels for a given unlabeled instance. Different well-known machine learning algorithms have been adapted to deal with the LR problem. In particular, fine-tuned instance-based algorithms (e.g., k-nearest neighbors) and model-based algorithms (e.g., decision trees) have performed remarkably well in tackling the LR problem. Probabilistic Graphical Models (PGMs, e.g., Bayesian networks) have not been considered to deal with this problem because of the difficulty of modeling permutations in that framework. In this paper, we propose a Hidden Naive Bayes classifier (HNB) to cope with the LR problem. By introducing a hidden variable, we can design a hybrid Bayesian network in which several types of distributions can be combined: multinomial for discrete variables, Gaussian for numerical variables, and Mallows for permutations. We consider two kinds of probabilistic models: one based on a Naive Bayes graphical structure (where only univariate probability distributions are estimated for each state of the hidden variable) and another where we allow interactions among the predictive attributes (using a multivariate Gaussian distribution for the parameter estimation). The experimental evaluation shows that our proposals are competitive with the start-of-the-art algorithms in both accuracy and in CPU time requirements.
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Li, Rita Yi Man, Kwong Wing Chau, and Frankie Fanjie Zeng. "Ranking of Risks for Existing and New Building Works." Sustainability 11, no. 10 (May 20, 2019): 2863. http://dx.doi.org/10.3390/su11102863.
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Site safety is one critical factor affecting the sustainability of skyscrapers and decoration, repair, and maintenance projects. Many newly-built skyscrapers exceed 50 storeys in Hong Kong and decoration, repair, and maintenance projects are widely performed to extend the lifespans of buildings. Although many cities do not contain skyscrapers at present, this will change in the future. Likewise, more decoration, repair, and maintenance projects will emerge. Thus, the present research, which compares the safety risks among the new and DSR projects, provides insights for builders, policymakers, and safety personnel. Moreover, research studies which rank and compare decoration, repair, and maintenance projects and new skyscraper constructions are scarce. The use of the evidence-based practice approach, which aims to narrow the gap between practice and academia in construction safety research, is the first of its kind. In this paper, we firstly provide a systematic literature review from 1999 to 2019 regarding construction safety, and then study the industry’s perspectives by analysing the construction practitioners’ interview results, court cases, and analytic hierarchy process survey results to compare them with the literature. It is found that the generation gap and prolonged working hours lead to accidents—a phenomenon which is unique in Hong Kong and absent from the literature. It also reveals that most accidents happen on new building sites due to tower crane failure, while those on DSR projects are linked with the circular saw. Although many of the contractors involved in new buildings are wealthier than DSR contractors, it is surprising to learn that lack of funding for safety is the most important factor linked to safety risks on the sites.
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Guo, Jiafeng, Yinqiong Cai, Yixing Fan, Fei Sun, Ruqing Zhang, and Xueqi Cheng. "Semantic Models for the First-Stage Retrieval: A Comprehensive Review." ACM Transactions on Information Systems 40, no. 4 (October 31, 2022): 1–42. http://dx.doi.org/10.1145/3486250.
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Multi-stage ranking pipelines have been a practical solution in modern search systems, where the first-stage retrieval is to return a subset of candidate documents and latter stages attempt to re-rank those candidates. Unlike re-ranking stages going through quick technique shifts over the past decades, the first-stage retrieval has long been dominated by classical term-based models. Unfortunately, these models suffer from the vocabulary mismatch problem, which may block re-ranking stages from relevant documents at the very beginning. Therefore, it has been a long-term desire to build semantic models for the first-stage retrieval that can achieve high recall efficiently. Recently, we have witnessed an explosive growth of research interests on the first-stage semantic retrieval models. We believe it is the right time to survey current status, learn from existing methods, and gain some insights for future development. In this article, we describe the current landscape of the first-stage retrieval models under a unified framework to clarify the connection between classical term-based retrieval methods, early semantic retrieval methods, and neural semantic retrieval methods. Moreover, we identify some open challenges and envision some future directions, with the hope of inspiring more research on these important yet less investigated topics.
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Parikh, Devi, Adriana Kovashka, Amar Parkash, and Kristen Grauman. "Relative Attributes for Enhanced Human-Machine Communication." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (September 20, 2021): 2153–59. http://dx.doi.org/10.1609/aaai.v26i1.8443.
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We propose to model relative attributes that capture the relationships between images and objects in terms of human-nameable visual properties. For example, the models can capture that animal A is 'furrier' than animal B, or image X is 'brighter' than image B. Given training data stating how object/scene categories relate according to different attributes, we learn a ranking function per attribute. The learned ranking functions predict the relative strength of each property in novel images. We show how these relative attribute predictions enable a variety of novel applications, including zero-shot learning from relative comparisons, automatic image description, image search with interactive feedback, and active learning of discriminative classifiers. We overview results demonstrating these applications with images of faces and natural scenes. Overall, we find that relative attributes enhance the precision of communication between humans and computer vision algorithms, providing the richer language needed to fluidly "teach" a system about visual concepts.
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Tsur, Oren, and Ari Rappoport. "Don’t Let Me Be #Misunderstood: Linguistically Motivated Algorithm for Predicting the Popularity of Textual Memes." Proceedings of the International AAAI Conference on Web and Social Media 9, no. 1 (August 3, 2021): 426–35. http://dx.doi.org/10.1609/icwsm.v9i1.14603.
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Prediction of the popularity of online textual snippets gained much attention in recent years. In this paper we investigate some of the factors that contribute to popularity of specific phrases such as Twitter hashtags. We define a new prediction task and propose a linguistically motivated algorithm for accurate prediction of hashtag popularity. Our prediction algorithm successfully models the interplay between various constraints such as the length restriction, typing effort and ease of comprehension. Controlling for network structure and social aspects we get a glimpse into the processes that shape the way we produce language and coin new words. In order to learn the interactions between the constraints we cast the problem as a ranking task. We adapt Gradient Boosted Trees for learning ranking functions in order to predict the hashtags/neologisms to be accepted. Our results outperform several baseline algorithms including SVM-rank, while maintaining higher interpretability, thus our model's prediction power can be used for better crafting of future hashtags.
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Bitan, Moshe, Ya’akov Gal, Sarit Kraus, Elad Dokow, and Amos Azaria. "Social Rankings in Human-Computer Committees." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (June 30, 2013): 116–22. http://dx.doi.org/10.1609/aaai.v27i1.8610.
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Despite committees and elections being widespread in thereal-world, the design of agents for operating in humancomputer committees has received far less attention than thetheoretical analysis of voting strategies. We address this gapby providing an agent design that outperforms other voters ingroups comprising both people and computer agents. In oursetting participants vote by simultaneously submitting a ranking over a set of candidates and the election system uses a social welfare rule to select a ranking that minimizes disagreements with participants’ votes. We ran an extensive studyin which hundreds of people participated in repeated votingrounds with other people as well as computer agents that differed in how they employ strategic reasoning in their votingbehavior. Our results show that over time, people learn todeviate from truthful voting strategies, and use heuristics toguide their play, such as repeating their vote from the previous round. We show that a computer agent using a bestresponse voting strategy was able to outperform people in thegame. Our study has implication for agent designers, highlighting the types of strategies that enable agents to succeedin committees comprising both human and computer participants. This is the first work to study the role of computeragents in voting settings involving both human and agent participants.
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Kang, Tae Yeob, Donghwan Seo, Yongchan Park, Joonki Min, and Taek-Soo Kim. "Early detection and instantaneous cause analysis of defects in interconnects by machine learning (ranking-CNN) of scattering parameter patterns." International Symposium on Microelectronics 2019, no. 1 (October 1, 2019): 000289–94. http://dx.doi.org/10.4071/2380-4505-2019.1.000289.
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Abstract This paper presents a novel method not only to detect defects in interconnects early but also to determine the root causes instantaneously by using machine learning of scattering parameter(s-parameter) patterns. it is found out that defective interconnects have certain and unique s-parameter patterns. We modified the facial age estimating algorithm (ranking-CNN) to learn the s-parameter patterns according to the cause and severity of defects in interconnects. Utilizing the training datasets obtained from pristine, cracked and photodegraded ITO specimens, our algorithm returns information on the cause of the interconnect defect and the severity level with accuracies of 95.56% and 91.11% respectively.
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Abu Khurma, Othman, Abdulla Al Darayseh, and Yahia Alramamneh. "A Framework for Incorporating the “Learning How to Learn” Approach in Teaching STEM Education." Education Sciences 13, no. 1 (December 20, 2022): 1. http://dx.doi.org/10.3390/educsci13010001.
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The importance of STEM subjects for the purposes of scientific and technological development has gained global momentum. Yet, there are various obstacles to applying a STEM education in the context of preparing students for a scientifically and technologically advanced society. There has been little research on how engineering can be incorporated into the elementary school curriculum. This study, thus, provides a systematic review of the learning techniques and structured framework that are used to support learning in the fields of science, technology, engineering, and mathematics (STEM). It also aids in identifying students’ shifts in interest toward STEM subjects, as well as their desire to pursue future STEM-based careers. This study makes use of a systematic literature review of high-impact journals with a Q1 or Q2 ranking. It was also found that there are hurdles in regard to the teaching approach that is used for STEM subjects. This suggests that there are numerous opportunities that can be exploited by educators in their hunt for a better STEM teaching approach. Finally, researchers must create features that enable students to gain fundamental competencies within the STEM disciplines. Future applications must include the experimental support for the purposes of inquiry-based learning activities.
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Boersma, Paul, and Bruce Hayes. "Empirical Tests of the Gradual Learning Algorithm." Linguistic Inquiry 32, no. 1 (January 2001): 45–86. http://dx.doi.org/10.1162/002438901554586.
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The Gradual Learning Algorithm (Boersma 1997) is a constraint-ranking algorithm for learning optimality-theoretic grammars. The purpose of this article is to assess the capabilities of the Gradual Learning Algorithm, particularly in comparison with the Constraint Demotion algorithm of Tesar and Smolensky (1993, 1996, 1998, 2000), which initiated the learnability research program for Optimality Theory. We argue that the Gradual Learning Algorithm has a number of special advantages: it can learn free variation, deal effectively with noisy learning data, and account for gradient well-formedness judgments. The case studies we examine involve Ilokano reduplication and metathesis, Finnish genitive plurals, and the distribution of English light and dark /l/.