Crowdsourced Label Aggregation Using Bilayer Collaborative Clustering

Zhang, Jing; Sheng, Victor S.; Wu, Jian

doi:10.1109/tnnls.2018.2890148

Cited by 29 publications

(11 citation statements)

References 35 publications

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“…Any participant can use the network platform to improve ideas, solve problems, and get corresponding rewards. [21] proposed a novel bilayer collaborative clustering (BLCC) method for the label aggregation in crowdsourcing. [22] proposed a novel task bundling based incentive mechanism that dynamically bundles tasks with different popularity together to solve the participation unbalance problem.…”

Section: Poi Recommendation Algorithmsmentioning

confidence: 99%

Session-Based Graph Attention POI Recommendation Network

Zhang

Zhu

Yue

2022

Wireless Communications and Mobile Computing

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Point-of-interest (POI) recommendation which aims at predicting the locations that users may be interested in has attracted wide attentions due to the development of Internet of Things and location-based services. Although collaborative filtering based methods and deep neural network have gain great success in POI recommendation, data sparsity and cold start problem still exist. To this end, this paper proposes session-based graph attention network (SGANet for short) for POI recommendation by making use of regional information. Specifically, we first extract users’ features from the regional history check-in data in session windows. Then, we use graph attention network to learn users’ preferences for both POI and regional POI, respectively. We learn the long-term and short-term preferences of users by fusing the user embedding and POI ancillary information through gate recurrent unit. Finally, we conduct experiments on two real world location-based social network datasets Foursquare and Gowalla to verify the effectiveness of the proposed recommendation model and the experiments results show that SGANet outperformed the compared baseline models in terms of recommendation accuracy, especially in sparse data and cold start scenario.

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Section: Poi Recommendation Algorithmsmentioning

confidence: 99%

Session-Based Graph Attention POI Recommendation Network

Zhang

Zhu

Yue

2022

Wireless Communications and Mobile Computing

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“…Guha et al (2015) and Chamberlain et al (2016) attempted to collect coreference annotations from non-expert crowd annotators. Even though crowd aggregation has been studied for many years, most existing studies have focused on aggregating classification labels (Dawid and Skene, 1979;Snow et al, 2008;Raykar et al, 2010;Hovy et al, 2013;Li et al, 2014;Felt et al, 2015;Zheng et al, 2017;Yin et al, 2017;Rodrigues and Pereira, 2018;Guan et al, 2018;Li et al, 2019;Zhang et al, 2019) or sequence labels (Hovy et al, 2014;Rodrigues et al, 2014;Huang et al, 2015;Nye et al, 2018;Lin et al, 2019). Note that Raykar et al (2010) and Felt et al (2015) also included contextual information.…”

Section: Related Workmentioning

confidence: 99%

A Neural Model for Aggregating Coreference Annotation in Crowdsourcing

Takamura

Ananiadou

2020

Proceedings of the 28th International Conference on Computational Linguistics

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Coreference resolution is the task of identifying all mentions in a text that refer to the same real-world entity. Collecting sufficient labelled data from expert annotators to train a highperformance coreference resolution system is time-consuming and expensive. Crowdsourcing makes it possible to obtain the required amounts of data rapidly and cost-effectively. However, crowd-sourced labels can be noisy. To ensure high-quality data, it is crucial to infer the correct labels by aggregating the noisy labels. In this paper, we split the aggregation into two subtasks, i.e, mention classification and coreference chain inference. Firstly, we predict the general class of each mention using an autoencoder, which incorporates contextual information about each mention, while at the same time taking into account the mention's annotation complexity and annotators' reliability at different levels. Secondly, to determine the coreference chain of each mention, we use weighted voting which takes into account the learned reliability in the first subtask. Experimental results demonstrate the effectiveness of our method in predicting the correct labels. We also illustrate our model's interpretability through a comprehensive analysis of experimental results.

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“…A recent work utilizes traditional clustering methods for truth inference [47]. The method succeeds on small datasets with well-formed clusters, but this may not be the case of large and sparse datasets where a few clusters corresponding to the label categories cannot separate the dataset well (e.g.…”

Section: Related Workmentioning

confidence: 99%

Truth Inference With a Deep Clustering-Based Aggregation Model

et al. 2020

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Traditional truth inference algorithms take multiple source labels as input and infer true labels for objects. Besides source labels, object features have been introduced in inference algorithms to achieve superior performance. A typical algorithm such as learning from crowds learns a classification model with the guide of inferred true labels where true labels are inferred from source labels. However, the main shortcoming exists in current algorithms and limits their inference performance: label noise. Since source labels from real-world data are noisy, a classifier is likely to be misguided to learn an imprecise decision boundary. In this paper, we propose a deep clustering-based aggregation model (DCAM) to overcome the shortcoming. DCAM introduces clustering for object features to form fine-grained clusters, where objects in the same cluster are supposed to have similar labels. DCAM exploits a cluster label distribution to represent the labeling information of all objects in the corresponding cluster to overcome the problem of label noise. To implement the idea of clustering-based truth inference, DCAM integrates source label generation and deep clustering in a unified framework by utilizing maximum a posteriori (MAP) estimation. Therefore, the proposed model is a novel approach for truth inference with object features. Experimental results on eight real-world inference tasks show that DCAM has a significant improvement of inference accuracy over the state-of-the-art truth inference algorithms. We further discuss the effect of cluster numbers, the quality of clustering, and illustrate the learned embeddings to support the effectiveness of DCAM. INDEX TERMS Crowdsourcing, truth inference, clustering methods, neural networks, unsupervised learning, machine learning.

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Crowdsourced Label Aggregation Using Bilayer Collaborative Clustering

Cited by 29 publications

References 35 publications

Session-Based Graph Attention POI Recommendation Network

Session-Based Graph Attention POI Recommendation Network

A Neural Model for Aggregating Coreference Annotation in Crowdsourcing

Truth Inference With a Deep Clustering-Based Aggregation Model

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