Готові списки джерел за темами / Ranking to Learn

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Добірка наукової літератури з теми "Ranking to Learn"

Автор: Grafiati

Опубліковано: 11 березня 2023

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Статті в журналах з теми "Ranking to Learn"

Yu, Yonghong, Li Zhang, Can Wang, Rong Gao, Weibin Zhao, and Jing Jiang. "Neural Personalized Ranking via Poisson Factor Model for Item Recommendation." Complexity 2019 (January 3, 2019): 1–16. http://dx.doi.org/10.1155/2019/3563674.

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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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Анотація:

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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Анотація:

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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Анотація:

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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Анотація:

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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Більше джерел

Дисертації з теми "Ranking to Learn"

Lin, Xiao. "Leveraging Multimodal Perspectives to Learn Common Sense for Vision and Language Tasks." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/79521.

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Learning and reasoning with common sense is a challenging problem in Artificial Intelligence (AI). Humans have the remarkable ability to interpret images and text from different perspectives in multiple modalities, and to use large amounts of commonsense knowledge while performing visual or textual tasks. Inspired by that ability, we approach commonsense learning as leveraging perspectives from multiple modalities for images and text in the context of vision and language tasks. Given a target task (e.g., textual reasoning, matching images with captions), our system first represents input images and text in multiple modalities (e.g., vision, text, abstract scenes and facts). Those modalities provide different perspectives to interpret the input images and text. And then based on those perspectives, the system performs reasoning to make a joint prediction for the target task. Surprisingly, we show that interpreting textual assertions and scene descriptions in the modality of abstract scenes improves performance on various textual reasoning tasks, and interpreting images in the modality of Visual Question Answering improves performance on caption retrieval, which is a visual reasoning task. With grounding, imagination and question-answering approaches to interpret images and text in different modalities, we show that learning commonsense knowledge from multiple modalities effectively improves the performance of downstream vision and language tasks, improves interpretability of the model and is able to make more efficient use of training data. Complementary to the model aspect, we also study the data aspect of commonsense learning in vision and language. We study active learning for Visual Question Answering (VQA) where a model iteratively grows its knowledge through querying informative questions about images for answers. Drawing analogies from human learning, we explore cramming (entropy), curiosity-driven (expected model change), and goal-driven (expected error reduction) active learning approaches, and propose a new goal-driven scoring function for deep VQA models under the Bayesian Neural Network framework. Once trained with a large initial training set, a deep VQA model is able to efficiently query informative question-image pairs for answers to improve itself through active learning, saving human effort on commonsense annotations.
Ph. D.

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ROFFO, GIORGIO. "Ranking to Learn and Learning to Rank: On the Role of Ranking in Pattern Recognition Applications." Doctoral thesis, 2017. http://hdl.handle.net/11562/960962.

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The last decade has seen a revolution in the theory and application of machine learning and pattern recognition. Through these advancements, variable ranking has emerged as an active and growing research area and it is now beginning to be applied to many new problems. The rationale behind this fact is that many pattern recognition problems are by nature ranking problems. The main objective of a ranking algorithm is to sort objects according to some criteria, so that, the most relevant items will appear early in the produced result list. Ranking methods can be analysed from two different methodological perspectives: ranking to learn and learning to rank. The former aims at studying methods and techniques to sort objects for improving the accuracy of a machine learning model. Enhancing a model performance can be challenging at times. For example, in pattern classification tasks, different data representations can complicate and hide the different explanatory factors of variation behind the data. In particular, hand-crafted features contain many cues that are either redundant or irrelevant, which turn out to reduce the overall accuracy of the classifier. In such a case feature selection is used, that, by producing ranked lists of features, helps to filter out the unwanted information. Moreover, in real-time systems (e.g., visual trackers) ranking approaches are used as optimization procedures which improve the robustness of the system that deals with the high variability of the image streams that change over time. The other way around, learning to rank is necessary in the construction of ranking models for information retrieval, biometric authentication, re-identification, and recommender systems. In this context, the ranking model's purpose is to sort objects according to their degrees of relevance, importance, or preference as defined in the specific application. This thesis addresses these issues and discusses different aspects of variable ranking in pattern recognition, biometrics, and computer vision. In particular, this work explores the merits of ranking to learn, by proposing novel solutions in feature selection that efficiently remove unwanted cues from the information stream. A novel graph-based ranking framework is proposed that exploits the convergence properties of power series of matrices thereby individuating candidate features, which turn out to be effective from a classification point of view. Moreover, it investigates the difficulties of ranking in real-time while presenting interesting solutions to better handle data variability in an important computer vision setting: Visual Object Tracking. The second part of this thesis focuses on the problem of learning to rank. Firstly, an interesting scenario of automatic user re-identification and verification in text chats is considered. Here, we start from the challenging problem of feature handcrafting to automatic feature learning solutions. We explore different techniques which turn out to produce effective ranks, contributing to push forward the state of the art. Moreover, we focus on advert recommendation, where deep convolutional neural networks with shallow architectures are used to rank ads according to users' preferences. We demonstrate the quality of our solutions in extensive experimental evaluations. Finally, this thesis introduces representative datasets and code libraries in different research areas that facilitate large-scale performance evaluation.

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Sandupatla, Hareesh. "Using reinforcement learning to learn relevance ranking of search queries." Thesis, 2016. http://hdl.handle.net/1805/11008.

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Indiana University-Purdue University Indianapolis (IUPUI)
Web search has become a part of everyday life for hundreds of millions of users around the world. However, the effectiveness of a user's search depends vitally on the quality of search result ranking. Even though enormous efforts have been made to improve the ranking quality, there is still significant misalignment between search engine ranking and an end user's preference order. This is evident from the fact that, for many search results on major search and e-commerce platforms, many users ignore the top ranked results and click on the lower ranked results. Nevertheless, finding a ranking that suits all the users is a difficult problem to solve as every user's need is different. So, an ideal ranking is the one which is preferred by the majority of the users. This emphasizes the need for an automated approach which improves the search engine ranking dynamically by incorporating user clicks in the ranking algorithm. In existing search result ranking methodologies, this direction has not been explored profoundly. A key challenge in using user clicks in search result ranking is that the relevance feedback that is learnt from click data is imperfect. This is due to the fact that a user is more likely to click a top ranked result than a lower ranked result, irrespective of the actual relevance of those results. This phenomenon is known as position bias which poses a major difficulty in obtaining an automated method for dynamic update of search rank orders. In my thesis, I propose a set of methodologies which incorporate user clicks for dynamic update of search rank orders. The updates are based on adaptive randomization of results using reinforcement learning strategy by considering the user click activities as reinforcement signal. Beginning at any rank order of the search results, the proposed methodologies guaranty to converge to a ranking which is close to the ideal rank order. Besides, the usage of reinforcement learning strategy enables the proposed methods to overcome the position bias phenomenon. To measure the effectiveness of the proposed method, I perform experiments considering a simplified user behavior model which I call color ball abstraction model. I evaluate the quality of the proposed methodologies using standard information retrieval metrics like Precision at n (P@n), Kendall tau rank correlation, Discounted Cumulative Gain (DCG) and Normalized Discounted Cumulative Gain (NDCG). The experiment results clearly demonstrate the success of the proposed methodologies.

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Книги з теми "Ranking to Learn"

Smith, Lasse Martin. Solid Ranking : Search Engine Optimization: Learn SEO - Search Engine Optimization. CreateSpace Independent Publishing Platform, 2015.

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Donnelly, Kevon. Seo Blueprint: Learn the Secrets to Improve Your Ranking and Surpass Your Competitors. Independently Published, 2019.

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Ignite Your Linkedin Profile: Learn the Secrets to How Linkedin Ranking Really Works. Wittman Technology, LLC, 2019.

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Denis, Benjamin. Master Guide : SEO for Business: Learn How to Improve Your Ranking with Local Business, WooCommerce and Structured Data Types. Independently Published, 2022.

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Prints, Cloud. Search Engine Optimization Strategies: Learn the Unique Strategies for Researching and Using High-Ranking Keywords to Rank First in Search Engines // Internet Search Engine. Independently Published, 2022.

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Torbert, Adam. Digital Marketing Essentials: Learn about Digital Marketing and How to Use It to Leverage Technology to Get More Traffic, Boost Your Website Ranking and Build a Brand. Independently Published, 2019.

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Joseph, Kelly. Self Publishing, SEO and Social Media Marketing Guides : : Learn from a Best Seller How to Write, Publish and Market Best Selling Books on Facebook, Optimize Your Product's Search Engines Ranking. Independently Published, 2017.

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Storm, Thomas. Link Building for Beginners: Learn how to build links and improve your rankings. Createspace Independent Publishing Platform, 2016.

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Geasey, Richard. Get Found Now! Local Search Secrets Exposed: Learn How to Achieve High Rankings in Google, Yahoo and Bing. CreateSpace Independent Publishing Platform, 2009.

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Locke, Joseph. The Road to the Bible Belt. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190216283.003.0005.

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By the turn of the twentieth century, a cohort of clerical activists, plagued by notions of a widespread spiritual crisis, realized that religious authority in public life could be bolstered by the construction of new and powerful denominational bureaucracies, the pursuit of moral reforms such as prohibition, and by tackling head on the widely held anticlerical fears confronting religious activism in public life. Activists such as Methodist minister George C. Rankin would learn, for instance, that reclaiming historical memory—abolishing hostile associations with witch trials and inquisitions–could convince more and more Texans that government could—and should—be run along religious lines. Moral reform was only the most public manifestation of a brewing clerical movement that targeted the popular religious attitudes of everyday southerners to enable the construction of the Bible Belt.

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Частини книг з теми "Ranking to Learn"

Roffo, Giorgio, and Simone Melzi. "Ranking to Learn:." In New Frontiers in Mining Complex Patterns, 19–35. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61461-8_2.

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Borgulya, István. "Learn the Ranking of Precedence Cases." In The State of the Art in Computational Intelligence, 152–61. Heidelberg: Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1844-4_26.

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Herbst, Patricio. "Geometric Modeling Tasks and Opportunity to Learn Geometry: The Ranking Triangles Task Revisited." In Research in Mathematics Education, 123–43. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29215-7_7.

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Erkkilä, Tero, and Ossi Piironen. "What Counts as World Class? Global University Rankings and Shifts in Institutional Strategies." In Evaluating Education: Normative Systems and Institutional Practices, 171–96. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7598-3_11.

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AbstractGlobal university rankings have emerged as a benchmark of institutional success, setting standards for higher education policymaking and institutional practices. Nevertheless, only a marginal share of higher education institutions (HEI) are in a realistic position to be ranked as a ‘world-class’ institutions. In the European context, the global rankings have been used to highlight a performance gap between European and North American institutions. Here the focus has been on the HEIs in the top-100 positions, causing concerns over European higher education. This has also become a marker of world-class university. We analyze the strategies of 27 Northern European universities in different tiers to learn how they have adjusted to the reality of ranking. We conclude that the references to global rankings have increased between 2014 and 2018. At the same time, the references to rankings have become more implicit in nature. Nevertheless, we find that the discourse of global comparison and excellence has become more common in the strategies. There are also emerging references to the regional role of universities, which are apparent in the strategies of universities that are clearly outside the top-100 ranked institutions. However, this is also a reflection of the discourse of world-class university.

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Cavenaghi, Emanuele, Lorenzo Camaione, Paolo Minasi, Gabriele Sottocornola, Fabio Stella, and Markus Zanker. "A Re-rank Algorithm for Online Hotel Search." In Information and Communication Technologies in Tourism 2023, 53–64. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25752-0_5.

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AbstractRecommender Systems were created to support users in situations of information overload. However, users are consciously or unconsciously influenced by several factors in their decision-making. We analysed a historical dataset from a meta-search booking platform with the aim of exploring how these factors influence user choices in the context of online hotel search and booking. Specifically, we focused our study on the influence of (i) ranking position, (ii) number of reviews, (iii) average ratings and (iv) price when analysing users’ click behaviour. Our results confirmed conventional wisdom that position and price were the “two elephants in the room” heavily influencing user decision-making. Thus, they need to be taken into account when, for instance, trying to learn user preferences from clickstream data. Using the results coming from this analysis, we performed an online A/B test on this meta-search booking platform comparing the current policy with a price-based re-rank policy. Our online experiments suggested that, although in offline experiments items with lower prices tend to have a higher Click-Through Rate, in an online context a price-based re-rank was only capable to improve the Click-Through Rate metric for the first positions of the recommended lists.

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Chahal, Virender, M. S. Narwal, and Sachin Kumar. "Ranking of Lean Critical Success Factors in Manufacturing Industry: AHP Approach." In Advances in Materials and Mechanical Engineering, 411–19. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0673-1_34.

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Schmidt, William, Nathan Burroughs, Lee Cogan, and Richard Houang. "Are College Rankings an Indicator of Quality Education? Comparing Barron’s and TEDS-M." In International Perspectives on Teacher Knowledge, Beliefs and Opportunities to Learn, 503–14. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-6437-8_23.

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Sindhwani, Rahul, Punj Lata Singh, Vipin Kaushik, Sumit Sharma, Rakesh Kumar Phanden, and Devendra Kumar Prajapati. "Ranking of Factors for Integrated Lean, Green and Agile Manufacturing for Indian Manufacturing SMEs." In Lecture Notes in Mechanical Engineering, 203–19. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4565-8_18.

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Abate, Alessandro, Mirco Giacobbe, and Diptarko Roy. "Learning Probabilistic Termination Proofs." In Computer Aided Verification, 3–26. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81688-9_1.

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AbstractWe present the first machine learning approach to the termination analysis of probabilistic programs. Ranking supermartingales (RSMs) prove that probabilistic programs halt, in expectation, within a finite number of steps. While previously RSMs were directly synthesised from source code, our method learns them from sampled execution traces. We introduce the neural ranking supermartingale: we let a neural network fit an RSM over execution traces and then we verify it over the source code using satisfiability modulo theories (SMT); if the latter step produces a counterexample, we generate from it new sample traces and repeat learning in a counterexample-guided inductive synthesis loop, until the SMT solver confirms the validity of the RSM. The result is thus a sound witness of probabilistic termination. Our learning strategy is agnostic to the source code and its verification counterpart supports the widest range of probabilistic single-loop programs that any existing tool can handle to date. We demonstrate the efficacy of our method over a range of benchmarks that include linear and polynomial programs with discrete, continuous, state-dependent, multi-variate, hierarchical distributions, and distributions with undefined moments.

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Scott, Joseph, Aina Niemetz, Mathias Preiner, Saeed Nejati, and Vijay Ganesh. "MachSMT: A Machine Learning-based Algorithm Selector for SMT Solvers." In Tools and Algorithms for the Construction and Analysis of Systems, 303–25. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72013-1_16.

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AbstractIn this paper, we present MachSMT, an algorithm selection tool for Satisfiability Modulo Theories (SMT) solvers. MachSMT supports the entirety of the SMT-LIB language. It employs machine learning (ML) methods to construct both empirical hardness models (EHMs) and pairwise ranking comparators (PWCs) over state-of-the-art SMT solvers. Given an SMT formula $$\mathcal {I}$$ I as input, MachSMT leverages these learnt models to output a ranking of solvers based on predicted run time on the formula $$\mathcal {I}$$ I . We evaluate MachSMT on the solvers, benchmarks, and data obtained from SMT-COMP 2019 and 2020. We observe MachSMT frequently improves on competition winners, winning $$54$$ 54 divisions outright and up to a $$198.4$$ 198.4 % improvement in PAR-2 score, notably in logics that have broad applications (e.g., BV, LIA, NRA, etc.) in verification, program analysis, and software engineering. The MachSMT tool is designed to be easily tuned and extended to any suitable solver application by users. MachSMT is not a replacement for SMT solvers by any means. Instead, it is a tool that enables users to leverage the collective strength of the diverse set of algorithms implemented as part of these sophisticated solvers.

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Тези доповідей конференцій з теми "Ranking to Learn"

Bertolino, Antonia, Antonio Guerriero, Breno Miranda, Roberto Pietrantuono, and Stefano Russo. "Learning-to-rank vs ranking-to-learn." In ICSE '20: 42nd International Conference on Software Engineering. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3377811.3380369.

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Connes, Victor, Colin de la Higuera, and Hoel Le Capitaine. "What should I learn next? Ranking Educational Resources." In 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE, 2021. http://dx.doi.org/10.1109/compsac51774.2021.00026.

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Engilberge, Martin, Louis Chevallier, Patrick Perez, and Matthieu Cord. "SoDeep: A Sorting Deep Net to Learn Ranking Loss Surrogates." In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.01105.

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Wanigasekara, Nirandika, Yuxuan Liang, Siong Thye Goh, Ye Liu, Joseph Jay Williams, and David S. Rosenblum. "Learning Multi-Objective Rewards and User Utility Function in Contextual Bandits for Personalized Ranking." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/532.

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This paper tackles the problem of providing users with ranked lists of relevant search results, by incorporating contextual features of the users and search results, and learning how a user values multiple objectives. For example, to recommend a ranked list of hotels, an algorithm must learn which hotels are the right price for users, as well as how users vary in their weighting of price against the location. In our paper, we formulate the context-aware, multi-objective, ranking problem as a Multi-Objective Contextual Ranked Bandit (MOCR-B). To solve the MOCR-B problem, we present a novel algorithm, named Multi-Objective Utility-Upper Confidence Bound (MOU-UCB). The goal of MOU-UCB is to learn how to generate a ranked list of resources that maximizes the rewards in multiple objectives to give relevant search results. Our algorithm learns to predict rewards in multiple objectives based on contextual information (combining the Upper Confidence Bound algorithm for multi-armed contextual bandits with neural network embeddings), as well as learns how a user weights the multiple objectives. Our empirical results reveal that the ranked lists generated by MOU-UCB lead to better click-through rates, compared to approaches that do not learn the utility function over multiple reward objectives.

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Chen, Xuanang, Jian Luo, Ben He, Le Sun, and Yingfei Sun. "Towards Robust Dense Retrieval via Local Ranking Alignment." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/275.

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Dense retrieval (DR) has extended the employment of pre-trained language models, like BERT, for text ranking. However, recent studies have raised the robustness issue of DR model against query variations, like query with typos, along with non-trivial performance losses. Herein, we argue that it would be beneficial to allow the DR model to learn to align the relative positions of query-passage pairs in the representation space, as query variations cause the query vector to drift away from its original position, affecting the subsequent DR effectiveness. To this end, we propose RoDR, a novel robust DR model that learns to calibrate the in-batch local ranking of query variation to that of original query for the DR space alignment. Extensive experiments on MS MARCO and ANTIQUE datasets show that RoDR significantly improves the retrieval results on both the original queries and different types of query variations. Meanwhile, RoDR provides a general query noise-tolerate learning framework that boosts the robustness and effectiveness of various existing DR models. Our code and models are openly available at https://github.com/cxa-unique/RoDR.

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Kim, Yejin, Kwangseob Kim, Chanyoung Park, and Hwanjo Yu. "Sequential and Diverse Recommendation with Long Tail." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/380.

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Sequential recommendation is a task that learns a temporal dynamic of a user behavior in sequential data and predicts items that a user would like afterward. However, diversity has been rarely emphasized in the context of sequential recommendation. Sequential and diverse recommendation must learn temporal preference on diverse items as well as on general items. Thus, we propose a sequential and diverse recommendation model that predicts a ranked list containing general items and also diverse items without compromising significant accuracy.To learn temporal preference on diverse items as well as on general items, we cluster and relocate consumed long tail items to make a pseudo ground truth for diverse items and learn the preference on long tail using recurrent neural network, which enables us to directly learn a ranking function. Extensive online and offline experiments deployed on a commercial platform demonstrate that our models significantly increase diversity while preserving accuracy compared to the state-of-the-art sequential recommendation model, and consequently our models improve user satisfaction.

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Zhao, Zhou, Lingtao Meng, Jun Xiao, Min Yang, Fei Wu, Deng Cai, Xiaofei He, and Yueting Zhuang. "Attentional Image Retweet Modeling via Multi-Faceted Ranking Network Learning." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/442.

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Retweet prediction is a challenging problem in social media sites (SMS). In this paper, we study the problem of image retweet prediction in social media, which predicts the image sharing behavior that the user reposts the image tweets from their followees. Unlike previous studies, we learn user preference ranking model from their past retweeted image tweets in SMS. We first propose heterogeneous image retweet modeling network (IRM) that exploits users' past retweeted image tweets with associated contexts, their following relations in SMS and preference of their followees. We then develop a novel attentional multi-faceted ranking network learning framework with multi-modal neural networks for the proposed heterogenous IRM network to learn the joint image tweet representations and user preference representations for prediction task. The extensive experiments on a large-scale dataset from Twitter site shows that our method achieves better performance than other state-of-the-art solutions to the problem.

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Ye, Mang, Zheng Wang, Xiangyuan Lan, and Pong C. Yuen. "Visible Thermal Person Re-Identification via Dual-Constrained Top-Ranking." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/152.

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Cross-modality person re-identification between the thermal and visible domains is extremely important for night-time surveillance applications. Existing works in this filed mainly focus on learning sharable feature representations to handle the cross-modality discrepancies. However, besides the cross-modality discrepancy caused by different camera spectrums, visible thermal person re-identification also suffers from large cross-modality and intra-modality variations caused by different camera views and human poses. In this paper, we propose a dual-path network with a novel bi-directional dual-constrained top-ranking loss to learn discriminative feature representations. It is advantageous in two aspects: 1) end-to-end feature learning directly from the data without extra metric learning steps, 2) it simultaneously handles the cross-modality and intra-modality variations to ensure the discriminability of the learnt representations. Meanwhile, identity loss is further incorporated to model the identity-specific information to handle large intra-class variations. Extensive experiments on two datasets demonstrate the superior performance compared to the state-of-the-arts.

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Alves, Marcos Antonio, Ivan Reinaldo Meneghini, and Frederico Gadelha Guimarães. "Learning Pairwise Comparisons with Machine Learning for Large-Scale Multi-Criteria Decision Making Problems." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2021. http://dx.doi.org/10.21528/cbic2021-13.

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Decision making is a complex task and requires a lot of cognitive effort from the decision maker. Multi-criteria methods, especially those based on pairwise comparisons, such as the Analytic Hierarchic Process (AHP), are not viable for large-scale decision-making problems. For this reason, the aim of this paper is to learn the preferences of the decision-maker using machine learning techniques in order to reduce the number of queries that are necessary in decision problems. We used a recently published parameterized generator of scalable and customizable benchmark problems for many-objective problems as a large-scale data generator. The proposed methodology is an iterative method in which a small subset of solutions are presented to the decision-maker to obtain pairwise judgments. This information is fed to an algorithm that learns the preferences for the remaining pairs in the decision matrix. The Gradient Boosting Regressor was applied in a problem with 5 criteria and 210 solutions. Subsets of 5, 7 and 10 solutions were used in each iteration. The metrics MSE, RMSE, MAPE and R2 were calculated. After the 8th iteration the ranking similarity stabilized, as measured by the tau distance. As the main advantage of the proposed approach is that it was necessary only 8 iterations presenting 5 solutions per time to learn the preferences and get an accurate final ranking.

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Sun, Zhu, Jie Yang, Jie Zhang, Alessandro Bozzon, Yu Chen, and Chi Xu. "MRLR: Multi-level Representation Learning for Personalized Ranking in Recommendation." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/391.

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Representation learning (RL) has recently proven to be effective in capturing local item relationships by modeling item co-occurrence in individual user's interaction record. However, the value of RL for recommendation has not reached the full potential due to two major drawbacks: 1) recommendation is modeled as a rating prediction problem but should essentially be a personalized ranking one; 2) multi-level organizations of items are neglected for fine-grained item relationships. We design a unified Bayesian framework MRLR to learn user and item embeddings from a multi-level item organization, thus benefiting from RL as well as achieving the goal of personalized ranking. Extensive validation on real-world datasets shows that MRLR consistently outperforms state-of-the-art algorithms.

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Звіти організацій з теми "Ranking to Learn"

Oxfam’s “Behind the Brands” Campaign: How a scorecard ranking, corporate engagement, and consumer activism catalyzed the largest food and beverage companies to change their ways. Population Council, 2017. http://dx.doi.org/10.31899/sbsr2017.1001.

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This case study describes how a scorecard ranking, corporate engagement, and consumer activism catalyzed the largest food and beverage companies to change their ways. This case study is part of a broader analysis on key lessons women’s health advocates can learn from the environmental movement on effective strategies for driving changes in corporate policies and practices.

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