In real-world recommendation scenarios, there are two common phenomena: 1) users only provide ratings but there is no review comment. As a result, the historical transaction data available for recommender system are usually unbalanced and sparse; 2) Users' opinions can be better grasped in their reviews than ratings. This indicates that there is always a bias between ratings and reviews. Therefore, it is important that users' ratings and reviews should be mutually reinforced to grasp the users' true opinions. To this end, in this paper, we develop an opinion mining model based on convolutional neural networks for enhancing recommendation (NeuO). Specifically, we exploit a two-step training neural networks, which utilize both reviews and ratings to grasp users' true opinions in unbalanced data. Moreover, we propose a Sentiment Classification scoring method (SC), which employs dual attention vectors to predict the users' sentiment scores of their reviews. A combination function is designed to use the results of SC and user-item rating matrix to catch the opinion bias. Finally, a Multilayer perceptron based Matrix Factorization (MMF) method is proposed to make recommendations with the enhanced user-item matrix. Extensive experiments on real-world data demonstrate that our approach can achieve a superior performance over state-of-the-art baselines on real-world datasets.
{"title":"Exploiting the Sentimental Bias between Ratings and Reviews for Enhancing Recommendation","authors":"Yuanbo Xu, Yongjian Yang, Jiayu Han, E. Wang, Fuzhen Zhuang, Hui Xiong","doi":"10.1109/ICDM.2018.00185","DOIUrl":"https://doi.org/10.1109/ICDM.2018.00185","url":null,"abstract":"In real-world recommendation scenarios, there are two common phenomena: 1) users only provide ratings but there is no review comment. As a result, the historical transaction data available for recommender system are usually unbalanced and sparse; 2) Users' opinions can be better grasped in their reviews than ratings. This indicates that there is always a bias between ratings and reviews. Therefore, it is important that users' ratings and reviews should be mutually reinforced to grasp the users' true opinions. To this end, in this paper, we develop an opinion mining model based on convolutional neural networks for enhancing recommendation (NeuO). Specifically, we exploit a two-step training neural networks, which utilize both reviews and ratings to grasp users' true opinions in unbalanced data. Moreover, we propose a Sentiment Classification scoring method (SC), which employs dual attention vectors to predict the users' sentiment scores of their reviews. A combination function is designed to use the results of SC and user-item rating matrix to catch the opinion bias. Finally, a Multilayer perceptron based Matrix Factorization (MMF) method is proposed to make recommendations with the enhanced user-item matrix. Extensive experiments on real-world data demonstrate that our approach can achieve a superior performance over state-of-the-art baselines on real-world datasets.","PeriodicalId":286444,"journal":{"name":"2018 IEEE International Conference on Data Mining (ICDM)","volume":"10 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125933450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Building sparse combinatorial model with non-negative constraint is essential in solving real-world problems such as in biology, in which the target response is often formulated by additive linear combination of features variables. This paper presents a solution to this problem by combining itemset mining with non-negative least squares. However, once incorporation of modern regularization is considered, then a naive solution requires to solve expensive enumeration problem many times for every regularization parameter. In this paper, we devise a regularization path tracking algorithm such that combinatorial feature is searched and included one by one to the solution set. Our contribution is a proposal of novel bounds specifically designed for the feature search problem. In synthetic dataset, the proposed method is demonstrated to run orders of magnitudes faster than a naive counterpart which does not employ tree pruning. We also empirically show that non-negativity constraints can reduce the number of active features much less than that of LASSO, leading to significant speed-ups in pattern search. In experiments using HIV-1 drug resistance dataset, the proposed method could successfully model the rapidly increasing drug resistance triggered by accumulation of mutations in HIV-1 genetic sequences. We also demonstrate the effectiveness of non-negativity constraints in suppressing false positive features, resulting in a model with smaller number of features and thereby improved interpretability.
{"title":"Entire Regularization Path for Sparse Nonnegative Interaction Model","authors":"Mirai Takayanagi, Yasuo Tabei, Hiroto Saigo","doi":"10.1109/ICDM.2018.00168","DOIUrl":"https://doi.org/10.1109/ICDM.2018.00168","url":null,"abstract":"Building sparse combinatorial model with non-negative constraint is essential in solving real-world problems such as in biology, in which the target response is often formulated by additive linear combination of features variables. This paper presents a solution to this problem by combining itemset mining with non-negative least squares. However, once incorporation of modern regularization is considered, then a naive solution requires to solve expensive enumeration problem many times for every regularization parameter. In this paper, we devise a regularization path tracking algorithm such that combinatorial feature is searched and included one by one to the solution set. Our contribution is a proposal of novel bounds specifically designed for the feature search problem. In synthetic dataset, the proposed method is demonstrated to run orders of magnitudes faster than a naive counterpart which does not employ tree pruning. We also empirically show that non-negativity constraints can reduce the number of active features much less than that of LASSO, leading to significant speed-ups in pattern search. In experiments using HIV-1 drug resistance dataset, the proposed method could successfully model the rapidly increasing drug resistance triggered by accumulation of mutations in HIV-1 genetic sequences. We also demonstrate the effectiveness of non-negativity constraints in suppressing false positive features, resulting in a model with smaller number of features and thereby improved interpretability.","PeriodicalId":286444,"journal":{"name":"2018 IEEE International Conference on Data Mining (ICDM)","volume":"26 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113980420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Huifang Ma, Di Zhang, Weizhong Zhao, Yanru Wang, Zhongzhi Shi
Recommending valuable contents for microblog users is an important way to improve users' experiences. As high quality descriptors of user semantics, tags have always been used to represent users' interests or attributes. In this work, we propose a microblog recommendation approach via hypergraph random walk tag expansion and user social relation. More specifically, microblogs are considered as hyperedges and terms are taken as hypervertexs for each user, and the weighting strategies for both hyperedges and hypervertexs are established. Random walk is performed on the weighted hypergraph to obtain a number of terms as tags for users. And then the tag similarity matrix and the user-tag matrix can be constructed based on tag probability correlations and weight of each tag. Besides, the significance of user social relation is also considered for recommendation. Moreover, an iterative updating scheme is developed to get the final user-tag matrix for computing the similarities between microblogs and users. Experimental results show that the algorithm is effective in microblog recommendation.
{"title":"Leveraging Hypergraph Random Walk Tag Expansion and User Social Relation for Microblog Recommendation","authors":"Huifang Ma, Di Zhang, Weizhong Zhao, Yanru Wang, Zhongzhi Shi","doi":"10.1109/ICDM.2018.00152","DOIUrl":"https://doi.org/10.1109/ICDM.2018.00152","url":null,"abstract":"Recommending valuable contents for microblog users is an important way to improve users' experiences. As high quality descriptors of user semantics, tags have always been used to represent users' interests or attributes. In this work, we propose a microblog recommendation approach via hypergraph random walk tag expansion and user social relation. More specifically, microblogs are considered as hyperedges and terms are taken as hypervertexs for each user, and the weighting strategies for both hyperedges and hypervertexs are established. Random walk is performed on the weighted hypergraph to obtain a number of terms as tags for users. And then the tag similarity matrix and the user-tag matrix can be constructed based on tag probability correlations and weight of each tag. Besides, the significance of user social relation is also considered for recommendation. Moreover, an iterative updating scheme is developed to get the final user-tag matrix for computing the similarities between microblogs and users. Experimental results show that the algorithm is effective in microblog recommendation.","PeriodicalId":286444,"journal":{"name":"2018 IEEE International Conference on Data Mining (ICDM)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115212088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Keyphrases have been widely used in large document collections for providing a concise summary of document content. While significant efforts have been made on the task of automatic keyphrase extraction, existing methods have challenges in training a robust supervised model when there are insufficient labeled data in the resource-poor domains. To this end, in this paper, we propose a novel Topic-based Adversarial Neural Network (TANN) method, which aims at exploiting the unlabeled data in the target domain and the data in the resource-rich source domain. Specifically, we first explicitly incorporate the global topic information into the document representation using a topic correlation layer. Then, domain-invariant features are learned to allow the efficient transfer from the source domain to the target by utilizing adversarial training on the topic-based representation. Meanwhile, to balance the adversarial training and preserve the domain-private features in the target domain, we reconstruct the target data from both forward and backward directions. Finally, based on the learned features, keyphrase are extracted using a tagging method. Experiments on two realworld cross-domain scenarios demonstrate that our method can significantly improve the performance of keyphrase extraction on unlabeled or insufficiently labeled target domain.
{"title":"Exploiting Topic-Based Adversarial Neural Network for Cross-Domain Keyphrase Extraction","authors":"Yanan Wang, Qi Liu, Chuan Qin, Tong Xu, Yijun Wang, Enhong Chen, Hui Xiong","doi":"10.1109/ICDM.2018.00075","DOIUrl":"https://doi.org/10.1109/ICDM.2018.00075","url":null,"abstract":"Keyphrases have been widely used in large document collections for providing a concise summary of document content. While significant efforts have been made on the task of automatic keyphrase extraction, existing methods have challenges in training a robust supervised model when there are insufficient labeled data in the resource-poor domains. To this end, in this paper, we propose a novel Topic-based Adversarial Neural Network (TANN) method, which aims at exploiting the unlabeled data in the target domain and the data in the resource-rich source domain. Specifically, we first explicitly incorporate the global topic information into the document representation using a topic correlation layer. Then, domain-invariant features are learned to allow the efficient transfer from the source domain to the target by utilizing adversarial training on the topic-based representation. Meanwhile, to balance the adversarial training and preserve the domain-private features in the target domain, we reconstruct the target data from both forward and backward directions. Finally, based on the learned features, keyphrase are extracted using a tagging method. Experiments on two realworld cross-domain scenarios demonstrate that our method can significantly improve the performance of keyphrase extraction on unlabeled or insufficiently labeled target domain.","PeriodicalId":286444,"journal":{"name":"2018 IEEE International Conference on Data Mining (ICDM)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115805917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jiawei Chen, C. Wang, M. Ester, Qihao Shi, Yan Feng, Chun Chen
With the explosive growth of online social networks, many social recommendation methods have been proposed and demonstrated that social information has potential to improve the recommendation performance. However, existing social recommendation methods always assume that the data is missing at random (MAR) but this is rarely the case. In fact, by analysing two real-world social recommendation datasets, we observed the following interesting phenomena: (1) users tend to consume and rate the items that they like and the items that have been consumed by their friends. (2) When the items have been consumed by more friends, the average values of the observed ratings will become smaller, not larger as assumed by the existing models. To model these phenomena, we integrate the missing not at random (MNAR) assumption in social recommendation and propose a new social recommendation method SPMF-MNAR, which models the observation process of rating data based on user's preference and social influence. Extensive experiments conducted on large real-world datasets validate that SPMF-MNAR achieves better performance than existing social recommendation methods and the non-social methods based on MNAR assumption.
随着在线社交网络的爆炸式增长，人们提出了许多社交推荐方法，并证明社交信息具有提高推荐性能的潜力。然而，现有的社会推荐方法总是假设数据随机缺失(MAR)，但这种情况很少发生。事实上，通过分析两个现实世界的社交推荐数据集，我们观察到以下有趣的现象:(1)用户倾向于消费和评价他们喜欢的商品和他们的朋友已经消费过的商品。(2)当该商品被更多的朋友消费时，观察到的评分平均值会变小，而不是像现有模型假设的那样变大。为了对这些现象进行建模，我们将缺失非随机假设(missing not at random, MNAR)融入到社会推荐中，提出了一种新的社会推荐方法SPMF-MNAR，该方法基于用户偏好和社会影响对评分数据的观察过程进行建模。在大型真实数据集上进行的大量实验验证了SPMF-MNAR比现有的社会推荐方法和基于MNAR假设的非社会推荐方法取得了更好的性能。
{"title":"Social Recommendation with Missing Not at Random Data","authors":"Jiawei Chen, C. Wang, M. Ester, Qihao Shi, Yan Feng, Chun Chen","doi":"10.1109/ICDM.2018.00018","DOIUrl":"https://doi.org/10.1109/ICDM.2018.00018","url":null,"abstract":"With the explosive growth of online social networks, many social recommendation methods have been proposed and demonstrated that social information has potential to improve the recommendation performance. However, existing social recommendation methods always assume that the data is missing at random (MAR) but this is rarely the case. In fact, by analysing two real-world social recommendation datasets, we observed the following interesting phenomena: (1) users tend to consume and rate the items that they like and the items that have been consumed by their friends. (2) When the items have been consumed by more friends, the average values of the observed ratings will become smaller, not larger as assumed by the existing models. To model these phenomena, we integrate the missing not at random (MNAR) assumption in social recommendation and propose a new social recommendation method SPMF-MNAR, which models the observation process of rating data based on user's preference and social influence. Extensive experiments conducted on large real-world datasets validate that SPMF-MNAR achieves better performance than existing social recommendation methods and the non-social methods based on MNAR assumption.","PeriodicalId":286444,"journal":{"name":"2018 IEEE International Conference on Data Mining (ICDM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114900947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Discovering community structure in networks remains a fundamentally challenging task. From scientific domains such as biology, chemistry and physics to social networks the challenge of identifying community structures in different kinds of network is challenging since there is no universal definition of community structure. Furthermore, with the surge of social networks, content information has played a pivotal role in defining community structure, demanding techniques beyond its traditional approach. Recently, network representation learning have shown tremendous promise. Leveraging on recent advances in deep learning, one can exploit deep learning's superiority to a network problem. Most predominantly, successes in supervised and semi-supervised task has shown promising results in network representation learning tasks such as link prediction and graph classification. However, much has yet to be explored in the literature of community detection which is an unsupervised learning task. This paper proposes a deep generative model for community detection and network generation. Empowered with Bayesian deep learning, deep generative models are capable of exploiting non-linearities while giving insights in terms of uncertainty. Hence, this paper proposes Variational Graph Autoencoder for Community Detection (VGAECD). Extensive experiment shows that it is capable of outperforming existing state-of-the-art methods. The generalization of the proposed model also allows the model to be considered as a graph generator. Additionally, unlike traditional methods, the proposed model does not require a predefined community structure definition. Instead, it assumes the existence of latent similarity between nodes and allows the model to find these similarities through an automatic model selection process. Optionally, it is capable of exploiting feature-rich information of a network such as node content, further increasing its performance.
{"title":"Learning Community Structure with Variational Autoencoder","authors":"Jun Jin Choong, Xin Liu, T. Murata","doi":"10.1109/ICDM.2018.00022","DOIUrl":"https://doi.org/10.1109/ICDM.2018.00022","url":null,"abstract":"Discovering community structure in networks remains a fundamentally challenging task. From scientific domains such as biology, chemistry and physics to social networks the challenge of identifying community structures in different kinds of network is challenging since there is no universal definition of community structure. Furthermore, with the surge of social networks, content information has played a pivotal role in defining community structure, demanding techniques beyond its traditional approach. Recently, network representation learning have shown tremendous promise. Leveraging on recent advances in deep learning, one can exploit deep learning's superiority to a network problem. Most predominantly, successes in supervised and semi-supervised task has shown promising results in network representation learning tasks such as link prediction and graph classification. However, much has yet to be explored in the literature of community detection which is an unsupervised learning task. This paper proposes a deep generative model for community detection and network generation. Empowered with Bayesian deep learning, deep generative models are capable of exploiting non-linearities while giving insights in terms of uncertainty. Hence, this paper proposes Variational Graph Autoencoder for Community Detection (VGAECD). Extensive experiment shows that it is capable of outperforming existing state-of-the-art methods. The generalization of the proposed model also allows the model to be considered as a graph generator. Additionally, unlike traditional methods, the proposed model does not require a predefined community structure definition. Instead, it assumes the existence of latent similarity between nodes and allows the model to find these similarities through an automatic model selection process. Optionally, it is capable of exploiting feature-rich information of a network such as node content, further increasing its performance.","PeriodicalId":286444,"journal":{"name":"2018 IEEE International Conference on Data Mining (ICDM)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125282949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rosni Lumbantoruan, Xiangmin Zhou, Yongli Ren, Z. Bao
Context-aware recommendation has emerged as perhaps the most popular service over online sites, and has seen applications to domains as diverse as entertainment, e-business, e-health and government services. There has been recent significant progress on the quality and scalability of recommender systems. However, we believe that different target users concern different contexts when they select an online item, which can greatly affect the quality of recommendation, and have not been investigated yet. In this paper, we propose a new type of recommender system, Declarative Context-Aware Recommender System (D-CARS), which enables the personalization of the contexts exploited for each target user by automatically analysing the viewing history of users. First, we propose a novel User-Window Non-negative Matrix Factorization topic model (UW-NMF) that adaptively identifies the significant contexts of users and constructs user profiles in a personalized manner. Then, we design a novel declarative context-aware recommendation algorithm that exploits the user context preference to identify a group of item candidates and its context distribution, based on a Subspace Ensemble Tree Model (SETM), which is constructed in the identified context subspace for item recommendation. Finally, we propose an algorithm that incrementally maintains our SETM model. Extensive experiments are conducted to prove the high effectiveness and efficiency of our D-CARS system.
{"title":"D-CARS: A Declarative Context-Aware Recommender System","authors":"Rosni Lumbantoruan, Xiangmin Zhou, Yongli Ren, Z. Bao","doi":"10.1109/ICDM.2018.00151","DOIUrl":"https://doi.org/10.1109/ICDM.2018.00151","url":null,"abstract":"Context-aware recommendation has emerged as perhaps the most popular service over online sites, and has seen applications to domains as diverse as entertainment, e-business, e-health and government services. There has been recent significant progress on the quality and scalability of recommender systems. However, we believe that different target users concern different contexts when they select an online item, which can greatly affect the quality of recommendation, and have not been investigated yet. In this paper, we propose a new type of recommender system, Declarative Context-Aware Recommender System (D-CARS), which enables the personalization of the contexts exploited for each target user by automatically analysing the viewing history of users. First, we propose a novel User-Window Non-negative Matrix Factorization topic model (UW-NMF) that adaptively identifies the significant contexts of users and constructs user profiles in a personalized manner. Then, we design a novel declarative context-aware recommendation algorithm that exploits the user context preference to identify a group of item candidates and its context distribution, based on a Subspace Ensemble Tree Model (SETM), which is constructed in the identified context subspace for item recommendation. Finally, we propose an algorithm that incrementally maintains our SETM model. Extensive experiments are conducted to prove the high effectiveness and efficiency of our D-CARS system.","PeriodicalId":286444,"journal":{"name":"2018 IEEE International Conference on Data Mining (ICDM)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126757732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Haripriya Harikumar, Santu Rana, Sunil Gupta, Thin Nguyen, R. Kaimal, S. Venkatesh
Privacy preservation is important. Prescriptive analytics is a method to extract corrective actions to avoid undesirable outcomes. We propose a privacy preserving prescriptive analytics algorithm to protect the data used during the construction of the prescriptive analytics algorithm. We use differential privacy mechanism to achieve strong privacy guarantee. Differential privacy mechanism requires computation of sensitivity: maximum change in the output between two training datasets, which is differed by only one instance. The main challenge we addressed is the computation of sensitivity of the prescription vector. In absence of any analytical form, we construct a nested global optimization problem to compute the sensitivity. We solve the optimization problem using constrained Bayesian optimization, as the nested structure makes the objective function expensive. We demonstrate our algorithm on two real world datasets and observe that the prescription vectors remains useful even after making them private.
{"title":"Differentially Private Prescriptive Analytics","authors":"Haripriya Harikumar, Santu Rana, Sunil Gupta, Thin Nguyen, R. Kaimal, S. Venkatesh","doi":"10.1109/ICDM.2018.00124","DOIUrl":"https://doi.org/10.1109/ICDM.2018.00124","url":null,"abstract":"Privacy preservation is important. Prescriptive analytics is a method to extract corrective actions to avoid undesirable outcomes. We propose a privacy preserving prescriptive analytics algorithm to protect the data used during the construction of the prescriptive analytics algorithm. We use differential privacy mechanism to achieve strong privacy guarantee. Differential privacy mechanism requires computation of sensitivity: maximum change in the output between two training datasets, which is differed by only one instance. The main challenge we addressed is the computation of sensitivity of the prescription vector. In absence of any analytical form, we construct a nested global optimization problem to compute the sensitivity. We solve the optimization problem using constrained Bayesian optimization, as the nested structure makes the objective function expensive. We demonstrate our algorithm on two real world datasets and observe that the prescription vectors remains useful even after making them private.","PeriodicalId":286444,"journal":{"name":"2018 IEEE International Conference on Data Mining (ICDM)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125129810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
From personalized ad delivery and healthcare to criminal sentencing, more decisions are made with help from methods developed in the fields of data mining and machine learning than ever before. However, their widespread use has raised concerns about the discriminatory impact which the methods may have on people subject to these decisions. Recently, imbalance in the misclassification rates between groups has been identified as a source of discrimination. Such discrimination is not handled by most existing work in discrimination-aware data mining, and it can persist even if other types of discrimination are alleviated. In this article, we present the Balancing Terms (BT) method to address this problem. BT balances the error rates of any classifier with a differentiable prediction function, and unlike existing work, it can incorporate a preference for the trade-off between fairness and accuracy. We empirically evaluate BT on real-world data, demonstrating that our method produces tradeoffs between error rate balance and total classification error that are superior and in only few cases comparable to the state-of-the-art.
{"title":"Using Balancing Terms to Avoid Discrimination in Classification","authors":"Simon Enni, I. Assent","doi":"10.1109/ICDM.2018.00116","DOIUrl":"https://doi.org/10.1109/ICDM.2018.00116","url":null,"abstract":"From personalized ad delivery and healthcare to criminal sentencing, more decisions are made with help from methods developed in the fields of data mining and machine learning than ever before. However, their widespread use has raised concerns about the discriminatory impact which the methods may have on people subject to these decisions. Recently, imbalance in the misclassification rates between groups has been identified as a source of discrimination. Such discrimination is not handled by most existing work in discrimination-aware data mining, and it can persist even if other types of discrimination are alleviated. In this article, we present the Balancing Terms (BT) method to address this problem. BT balances the error rates of any classifier with a differentiable prediction function, and unlike existing work, it can incorporate a preference for the trade-off between fairness and accuracy. We empirically evaluate BT on real-world data, demonstrating that our method produces tradeoffs between error rate balance and total classification error that are superior and in only few cases comparable to the state-of-the-art.","PeriodicalId":286444,"journal":{"name":"2018 IEEE International Conference on Data Mining (ICDM)","volume":"604 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125185613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jinzheng Tu, Guoxian Yu, C. Domeniconi, J. Wang, Guoqiang Xiao, Maozu Guo
Crowdsourcing is a useful and economic approach to data annotation. To obtain annotation of high quality, various aggregation approaches have been developed, which take into account different factors that impact the quality of aggregated answers. However, existing methods generally focus on single-label (multi-class and binary) tasks, and they ignore the inter-correlation between labels, and thus may have compromised quality. In this paper, we introduce a Multi-Label answer aggregation approach based on Joint Matrix Factorization (ML-JMF). ML-JMF selectively and jointly factorizes the sample-label association matrices collected from different annotators into products of individual and shared low-rank matrices. As such, it takes advantage of the robustness of low-rank matrix approximation to noise, and reduces the impact of unreliable annotators by assigning small (zero) weights to their annotation matrices. In addition, it takes advantage of the correlation among labels by leveraging the shared low-rank matrix, and of the similarity between annotators using the individual low-rank matrices to guide the factorization. ML-JMF pursues the low-rank matrices via a unified objective function, and introduces an iterative technique to optimize it. ML-JMF finally uses the optimized low-rank matrices and weights to infer the ground-truth labels. Our experimental results on multi-label datasets show that ML-JMF outperforms competitive methods in inferring ground truth labels. Our approach can identify unreliable annotators, and is robust against their misleading answers through the assignment of small (zero) weights to their annotation.
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