Distributed Ordinal Regression Over Networks

Liu, Huan; Tu, Jiankai; Li, Chunguang

doi:10.1109/access.2021.3074629

Cited by 3 publications

(4 citation statements)

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“…A plenty of research works are performed in ordinal regression [4,7,12,14,16,17,38]. Among these, Wenzhi et al [22] uses a neural network architecture with a non-traditional loss function which is particularly suited for the ordinal regression task.…”

Section: Related Workmentioning

confidence: 99%

ORDSIM: Ordinal Regression for E-Commerce Query Similarity Prediction

Kabir¹,

Hasan²,

Mandal³

et al. 2022

Preprint

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Query similarity prediction task is generally solved by regression based models with square loss. Such a model is agnostic of absolute similarity values and it penalizes the regression error at all ranges of similarity values at the same scale. However, to boost e-commerce platform's monetization, it is important to predict high-level similarity more accurately than low-level similarity, as highly similar queries retrieves items according to user-intents, whereas moderately similar item retrieves related items, which may not lead to a purchase. Regression models fail to customize its loss function to concentrate around the high-similarity band, resulting poor performance in query similarity prediction task. We address the above challenge by considering the query prediction as an ordinal regression problem, and thereby propose a model, ORDSIM (ORDinal Regression for SIMilarity Prediction). ORDSIM exploits variable-width buckets to model ordinal loss, which penalizes errors in high-level similarity harshly, and thus enable the regression model to obtain better prediction results for high similarity values. We evaluate ORDSIM on a dataset of over 10 millions e-commerce queries from eBay platform and show that ORDSIM achieves substantially smaller prediction error compared to the competing regression methods on this dataset.

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Section: Related Workmentioning

confidence: 99%

ORDSIM: Ordinal Regression for E-Commerce Query Similarity Prediction

Kabir¹,

Hasan²,

Mandal³

et al. 2022

Preprint

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“…Distributed methods were extensively studied in many fields, such as distributed estimation [ 21 , 22 ], distributed optimization [ 23 , 24 ], distributed clustering [ 25 ], distributed Kalman filter [ 26 ], and distributed anomaly detection [ 27 ]. However, as far as we know, there are few works investigating distributed ordinal regression [ 14 ]. In [ 14 ], the authors proposed a distributed generalized ordered logit model, which is a linear model and therefore cannot handle complex problems.…”

Section: Related Workmentioning

confidence: 99%

“…However, as far as we know, there are few works investigating distributed ordinal regression [ 14 ]. In [ 14 ], the authors proposed a distributed generalized ordered logit model, which is a linear model and therefore cannot handle complex problems.…”

Section: Related Workmentioning

confidence: 99%

“…The main contributions of this paper are summarized as follows. Existing work on distributed ordinal regression [ 14 ] uses a linear model; therefore, it cannot deal with the problems of linearly inseparable data. We extended the SVOR method to distributed scenarios to solve distributed ordinal regression problems with linearly inseparable data.…”

Section: Introductionmentioning

confidence: 99%

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Distributed Support Vector Ordinal Regression over Networks

Liu¹,

Tu²,

Li³

2022

Entropy

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Ordinal regression methods are widely used to predict the ordered labels of data, among which support vector ordinal regression (SVOR) methods are popular because of their good generalization. In many realistic circumstances, data are collected by a distributed network. In order to protect privacy or due to some practical constraints, data cannot be transmitted to a center for processing. However, as far as we know, existing SVOR methods are all centralized. In the above situations, centralized methods are inapplicable, and distributed methods are more suitable choices. In this paper, we propose a distributed SVOR (dSVOR) algorithm. First, we formulate a constrained optimization problem for SVOR in distributed circumstances. Since there are some difficulties in solving the problem with classical methods, we used the random approximation method and the hinge loss function to transform the problem into a convex optimization problem with constraints. Then, we propose subgradient-based algorithm dSVOR to solve it. To illustrate the effectiveness, we theoretically analyze the consensus and convergence of the proposed method, and conduct experiments on both synthetic data and a real-world example. The experimental results show that the proposed dSVOR could achieve close performance to that of the corresponding centralized method, which needs all the data to be collected together.

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