WSDM2018読み会 資料
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WSDM2018読み会 2018-04-14 @ クックパッド https://atnd.org/events/95510 Offline A/B Testing for Recommender Systems A. Gilotte, C. Calauzénes, T. Nedelec, A. Abraham, and S. Dollé https://doi.org/10.1145/3159652.3159687
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RecSys2018論文読み会 資料
Exploring Author Gender in Book Rating and Recommendation
M. D. Ekstrand, M. Tian, M. R. I. Kazi, H. Mehrpouyan, and D. Kluver
https://doi.org/10.1145/3240323.3240373
RecSys2018 論文読み会 (2018-11-17) https://atnd.org/events/101334
Recommendation Independence
Recommendation Independence
The 1st Conference on Fairness, Accountability, and Transparency
Article @ Official Site: http://proceedings.mlr.press/v81/kamishima18a.html
Conference site: https://fatconference.org/2018/
Abstract:
This paper studies a recommendation algorithm whose outcomes are not influenced by specified information. It is useful in contexts potentially unfair decision should be avoided, such as job-applicant recommendations that are not influenced by socially sensitive information. An algorithm that could exclude the influence of sensitive information would thus be useful for job-matching with fairness. We call the condition between a recommendation outcome and a sensitive feature Recommendation Independence, which is formally defined as statistical independence between the outcome and the feature. Our previous independence-enhanced algorithms simply matched the means of predictions between sub-datasets consisting of the same sensitive value. However, this approach could not remove the sensitive information represented by the second or higher moments of distributions. In this paper, we develop new methods that can deal with the second moment, i.e., variance, of recommendation outcomes without increasing the computational complexity. These methods can more strictly remove the sensitive information, and experimental results demonstrate that our new algorithms can more effectively eliminate the factors that undermine fairness. Additionally, we explore potential applications for independence-enhanced recommendation, and discuss its relation to other concepts, such as recommendation diversity.
機械学習研究でのPythonの利用
Pythonオープンサイエンスシンポジウム in つくば
https://startpython.connpass.com/event/70649/
2012-12-12
デモファイル： https://gist.github.com/tkamishima/8a8675f0f3eb687316513788379af92a
Considerations on Recommendation Independence for a Find-Good-Items Task
Considerations on Recommendation Independence for a Find-Good-Items Task
Workshop on Responsible Recommendation (FATREC), in conjunction with RecSys2017
Article @ Official Site: http://doi.org/10.18122/B2871W
Workshop Homepage: https://piret.gitlab.io/fatrec/
This paper examines the notion of recommendation independence, which is a constraint that a recommendation result is independent from specific information. This constraint is useful in ensuring adherence to laws and regulations, fair treatment of content providers, and exclusion of unwanted information. For example, to make a job-matching recommendation socially fair, the matching should be independent of socially sensitive information, such as gender or race. We previously developed several recommenders satisfying recommendation independence, but these were all designed for a predicting-ratings task, whose goal is to predict a score that a user would rate. We here focus on another find-good-items task, which aims to find some items that a user would prefer. In this task, scores representing the degree of preference to items are first predicted, and some items having the largest scores are displayed in the form of a ranked list. We developed a preliminary algorithm for this task through a naive approach, enhancing independence between a preference score and sensitive information. We empirically show that although this algorithm can enhance independence of a preference score, it is not fit for the purpose of enhancing independence in terms of a ranked list. This result indicates the need for inventing a notion of independence that is suitable for use with a ranked list and that is applicable for completing a find-good-items task.
Model-based Approaches for Independence-Enhanced Recommendation
Model-based Approaches for Independence-Enhanced Recommendation
IEEE International Workshop on Privacy Aspects of Data Mining (PADM), in conjunction with ICDM2016
Article @ Official Site: http://doi.ieeecomputersociety.org/10.1109/ICDMW.2016.0127
Workshop Homepage: http://pddm16.eurecat.org/
Abstract:
This paper studies a new approach to enhance recommendation independence. Such approaches are useful in ensuring adherence to laws and regulations, fair treatment of content providers, and exclusion of unwanted information. For example, recommendations that match an employer with a job applicant should not be based on socially sensitive information, such as gender or race, from the perspective of social fairness. An algorithm that could exclude the influence of such sensitive information would be useful in this case. We previously gave a formal definition of recommendation independence and proposed a method adopting a regularizer that imposes such an independence constraint. As no other options than this regularization approach have been put forward, we here propose a new model-based approach, which is based on a generative model that satisfies the constraint of recommendation independence. We apply this approach to a latent class model and empirically show that the model-based approach can enhance recommendation independence. Recommendation algorithms based on generative models, such as topic models, are important, because they have a flexible functionality that enables them to incorporate a wide variety of information types. Our new model-based approach will broaden the applications of independence-enhanced recommendation by integrating the functionality of generative models.
KDD2016勉強会 資料
KDD2016勉強会 https://atnd.org/events/80771
論文：“Why Should I Trust You?”Explaining the Predictions of Any Classifier
著者：M. T. Ribeiro and S. Singh and C. Guestrin
論文リンク： http://www.kdd.org/kdd2016/subtopic/view/why-should-i-trust-you-explaining-the-predictions-of-any-classifier
科学技術計算関連Pythonパッケージの概要
こちらが改訂版なので，ダウンロードするならこちらで： http://www.kamishima.net/archive/scipy-overview.pdf
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* 講演ページ： http://www.ipsj.or.jp/event/fit/fit2016/FIT2016program_web/data/html/event/event72.html
* 小嵜 耕平さん資料「Python とデータ分析コンテストの実践」 https://speakerdeck.com/smly/python-todetafen-xi-kontesutofalseshi-jian
* サンプルファイル： https://github.com/tkamishima/fit2016tutorial
WSDM2016勉強会 資料
WSDM2016勉強会 https://atnd.org/events/74341
論文：Portrait of an Online Shopper: Understanding and Predicting Consumer Behavior
著者：F. Kooti and K. Lerman and L. M. Aiello and M. Grbovic and N. Djuric
論文リンク： http://dx.doi.org/10.1145/2835776.2835831
Recommended
RecSys2018論文読み会 資料
Exploring Author Gender in Book Rating and Recommendation
M. D. Ekstrand, M. Tian, M. R. I. Kazi, H. Mehrpouyan, and D. Kluver
https://doi.org/10.1145/3240323.3240373
RecSys2018 論文読み会 (2018-11-17) https://atnd.org/events/101334
Recommendation Independence
Recommendation Independence
The 1st Conference on Fairness, Accountability, and Transparency
Article @ Official Site: http://proceedings.mlr.press/v81/kamishima18a.html
Conference site: https://fatconference.org/2018/
Abstract:
This paper studies a recommendation algorithm whose outcomes are not influenced by specified information. It is useful in contexts potentially unfair decision should be avoided, such as job-applicant recommendations that are not influenced by socially sensitive information. An algorithm that could exclude the influence of sensitive information would thus be useful for job-matching with fairness. We call the condition between a recommendation outcome and a sensitive feature Recommendation Independence, which is formally defined as statistical independence between the outcome and the feature. Our previous independence-enhanced algorithms simply matched the means of predictions between sub-datasets consisting of the same sensitive value. However, this approach could not remove the sensitive information represented by the second or higher moments of distributions. In this paper, we develop new methods that can deal with the second moment, i.e., variance, of recommendation outcomes without increasing the computational complexity. These methods can more strictly remove the sensitive information, and experimental results demonstrate that our new algorithms can more effectively eliminate the factors that undermine fairness. Additionally, we explore potential applications for independence-enhanced recommendation, and discuss its relation to other concepts, such as recommendation diversity.
機械学習研究でのPythonの利用
Pythonオープンサイエンスシンポジウム in つくば
https://startpython.connpass.com/event/70649/
2012-12-12
デモファイル： https://gist.github.com/tkamishima/8a8675f0f3eb687316513788379af92a
Considerations on Recommendation Independence for a Find-Good-Items Task
Considerations on Recommendation Independence for a Find-Good-Items Task
Workshop on Responsible Recommendation (FATREC), in conjunction with RecSys2017
Article @ Official Site: http://doi.org/10.18122/B2871W
Workshop Homepage: https://piret.gitlab.io/fatrec/
This paper examines the notion of recommendation independence, which is a constraint that a recommendation result is independent from specific information. This constraint is useful in ensuring adherence to laws and regulations, fair treatment of content providers, and exclusion of unwanted information. For example, to make a job-matching recommendation socially fair, the matching should be independent of socially sensitive information, such as gender or race. We previously developed several recommenders satisfying recommendation independence, but these were all designed for a predicting-ratings task, whose goal is to predict a score that a user would rate. We here focus on another find-good-items task, which aims to find some items that a user would prefer. In this task, scores representing the degree of preference to items are first predicted, and some items having the largest scores are displayed in the form of a ranked list. We developed a preliminary algorithm for this task through a naive approach, enhancing independence between a preference score and sensitive information. We empirically show that although this algorithm can enhance independence of a preference score, it is not fit for the purpose of enhancing independence in terms of a ranked list. This result indicates the need for inventing a notion of independence that is suitable for use with a ranked list and that is applicable for completing a find-good-items task.
Model-based Approaches for Independence-Enhanced Recommendation
Model-based Approaches for Independence-Enhanced Recommendation
IEEE International Workshop on Privacy Aspects of Data Mining (PADM), in conjunction with ICDM2016
Article @ Official Site: http://doi.ieeecomputersociety.org/10.1109/ICDMW.2016.0127
Workshop Homepage: http://pddm16.eurecat.org/
Abstract:
This paper studies a new approach to enhance recommendation independence. Such approaches are useful in ensuring adherence to laws and regulations, fair treatment of content providers, and exclusion of unwanted information. For example, recommendations that match an employer with a job applicant should not be based on socially sensitive information, such as gender or race, from the perspective of social fairness. An algorithm that could exclude the influence of such sensitive information would be useful in this case. We previously gave a formal definition of recommendation independence and proposed a method adopting a regularizer that imposes such an independence constraint. As no other options than this regularization approach have been put forward, we here propose a new model-based approach, which is based on a generative model that satisfies the constraint of recommendation independence. We apply this approach to a latent class model and empirically show that the model-based approach can enhance recommendation independence. Recommendation algorithms based on generative models, such as topic models, are important, because they have a flexible functionality that enables them to incorporate a wide variety of information types. Our new model-based approach will broaden the applications of independence-enhanced recommendation by integrating the functionality of generative models.
KDD2016勉強会 資料
KDD2016勉強会 https://atnd.org/events/80771
論文：“Why Should I Trust You?”Explaining the Predictions of Any Classifier
著者：M. T. Ribeiro and S. Singh and C. Guestrin
論文リンク： http://www.kdd.org/kdd2016/subtopic/view/why-should-i-trust-you-explaining-the-predictions-of-any-classifier
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* 講演ページ： http://www.ipsj.or.jp/event/fit/fit2016/FIT2016program_web/data/html/event/event72.html
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* サンプルファイル： https://github.com/tkamishima/fit2016tutorial
WSDM2016勉強会 資料
WSDM2016勉強会 https://atnd.org/events/74341
論文：Portrait of an Online Shopper: Understanding and Predicting Consumer Behavior
著者：F. Kooti and K. Lerman and L. M. Aiello and M. Grbovic and N. Djuric
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Future Directions of Fairness-Aware Data Mining: Recommendation, Causality, a...
Future Directions of Fairness-Aware Data Mining: Recommendation, Causality, and Theoretical Aspects
Invited Talk @ Workshop on Fairness, Accountability, and Transparency in Machine Learning
In conjunction with the ICML 2015 @ Lille, France, Jul. 11, 2015
Web Site: http://www.kamishima.net/fadm/
Handnote: http://www.kamishima.net/archive/2015-ws-icml-HN.pdf
The goal of fairness-aware data mining (FADM) is to analyze data while taking into account potential issues of fairness. In this talk, we will cover three topics in FADM:
1. Fairness in a Recommendation Context: In classification tasks, the term "fairness" is regarded as anti-discrimination. We will present other types of problems related to the fairness in a recommendation context.
2. What is Fairness: Most formal definitions of fairness have a connection with the notion of statistical independence. We will explore other types of formal fairness based on causality, agreement, and unfairness.
3. Theoretical Problems of FADM: After reviewing technical and theoretical open problems in the FADM literature, we will introduce the theory of the generalization bound in terms of accuracy as well as fairness.
Joint work with Jun Sakuma, Shotaro Akaho, and Hideki Asoh
Correcting Popularity Bias by Enhancing Recommendation Neutrality
Correcting Popularity Bias by Enhancing Recommendation Neutrality on
The 8th ACM Conference on Recommender Systems, Poster
Article @ Official Site: http://ceur-ws.org/Vol-1247/
Article @ Personal Site: http://www.kamishima.net/archive/2014-po-recsys-print.pdf
Abstract:
In this paper, we attempt to correct a popularity bias, which is the tendency for popular items to be recommended more frequently, by enhancing recommendation neutrality. Recommendation neutrality involves excluding specified information from the prediction process of recommendation. This neutrality was formalized as the statistical independence between a recommendation result and the specified information, and we developed a recommendation algorithm that satisfies this independence constraint. We correct the popularity bias by enhancing neutrality with respect to information regarding whether candidate items are popular or not. We empirically show that a popularity bias in the predicted preference scores can be corrected.
PyMCがあれば，ベイズ推定でもう泣いたりなんかしない
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関連資料： https://github.com/scipy-japan/tokyo-scipy/tree/master/006/shima__shima
The Independence of Fairness-aware Classifiers
The Independence of Fairness-aware Classifiers
IEEE International Workshop on Privacy Aspects of Data Mining (PADM), in conjunction with ICDM2013
Article @ Official Site:
Article @ Personal Site: http://www.kamishima.net/archive/2013-ws-icdm-print.pdf
Handnote : http://www.kamishima.net/archive/2013-ws-icdm-HN.pdf
Program codes : http://www.kamishima.net/fadm/
Workshop Homepage: http://www.cs.cf.ac.uk/padm2013/
Abstract:
Due to the spread of data mining technologies, such technologies are being used for determinations that seriously affect individuals' lives. For example, credit scoring is frequently determined based on the records of past credit data together with statistical prediction techniques. Needless to say, such determinations must be nondiscriminatory and fair in sensitive features, such as race, gender, religion, and so on. The goal of fairness-aware classifiers is to classify data while taking into account the potential issues of fairness, discrimination, neutrality, and/or independence. In this paper, after reviewing fairness-aware classification methods, we focus on one such method, Calders and Verwer's two-naive-Bayes method. This method has been shown superior to the other classifiers in terms of fairness, which is formalized as the statistical independence between a class and a sensitive feature. However, the cause of the superiority is unclear, because it utilizes a somewhat heuristic post-processing technique rather than an explicitly formalized model. We clarify the cause by comparing this method with an alternative naive Bayes classifier, which is modified by a modeling technique called "hypothetical fair-factorization." This investigation reveals the theoretical background of the two-naive-Bayes method and its connections with other methods. Based on these findings, we develop another naive Bayes method with an "actual fair-factorization" technique and empirically show that this new method can achieve an equal level of fairness as that of the two-naive-Bayes classifier.
Efficiency Improvement of Neutrality-Enhanced Recommendation
Efficiency Improvement of Neutrality-Enhanced Recommendation
Workshop on Human Decision Making in Recommender Systems, in conjunction with RecSys 2013
Article @ Official Site: http://ceur-ws.org/Vol-1050/
Article @ Personal Site: http://www.kamishima.net/archive/2013-ws-recsys-print.pdf
Handnote : http://www.kamishima.net/archive/2013-ws-recsys-HN.pdf
Program codes : http://www.kamishima.net/inrs/
Workshop Homepage: http://recex.ist.tugraz.at/RecSysWorkshop/
Abstract:
This paper proposes an algorithm for making recommendations so that neutrality from a viewpoint specified by the user is enhanced. This algorithm is useful for avoiding decisions based on biased information. Such a problem is pointed out as the filter bubble, which is the influence in social decisions biased by personalization technologies. To provide a neutrality-enhanced recommendation, we must first assume that a user can specify a particular viewpoint from which the neutrality can be applied, because a recommendation that is neutral from all viewpoints is no longer a recommendation. Given such a target viewpoint, we implement an information-neutral recommendation algorithm by introducing a penalty term to enforce statistical independence between the target viewpoint and a rating. We empirically show that our algorithm enhances the independence from the specified viewpoint.
Absolute and Relative Clustering
Absolute and Relative Clustering
4th MultiClust Workshop on Multiple Clusterings, Multi-view Data, and Multi-source Knowledge-driven Clustering (Multiclust 2013)
Aug. 11, 2013 @ Chicago, U.S.A, in conjunction with KDD2013
Article @ Official Site: http://dx.doi.org/10.1145/2501006.2501013
Article @ Personal Site: http://www.kamishima.net/archive/2013-ws-kdd-print.pdf
Handnote: http://www.kamishima.net/archive/2013-ws-kdd-HN.pdf
Workshop Homepage: http://cs.au.dk/research/research-areas/data-intensive-systems/projects/multiclust2013/
Abstract:
Research into (semi-)supervised clustering has been increasing. Supervised clustering aims to group similar data that are partially guided by the user's supervision. In this supervised clustering, there are many choices for formalization. For example, as a type of supervision, one can adopt labels of data points, must/cannot links, and so on. Given a real clustering task, such as grouping documents or image segmentation, users must confront the question ``How should we mathematically formalize our task?''To help answer this question, we propose the classification of real clusterings into absolute and relative clusterings, which are defined based on the relationship between the resultant partition and the data set to be clustered. This categorization can be exploited to choose a type of task formalization.
Consideration on Fairness-aware Data Mining
Consideration on Fairness-aware Data Mining
IEEE International Workshop on Discrimination and Privacy-Aware Data Mining (DPADM 2012)
Dec. 10, 2012 @ Brussels, Belgium, in conjunction with ICDM2012
Article @ Official Site: http://doi.ieeecomputersociety.org/10.1109/ICDMW.2012.101
Article @ Personal Site: http://www.kamishima.net/archive/2012-ws-icdm-print.pdf
Handnote: http://www.kamishima.net/archive/2012-ws-icdm-HN.pdf
Workshop Homepage: https://sites.google.com/site/dpadm2012/
Abstract:
With the spread of data mining technologies and the accumulation of social data, such technologies and data are being used for determinations that seriously affect individuals' lives. For example, credit scoring is frequently determined based on the records of past credit data together with statistical prediction techniques. Needless to say, such determinations must be nondiscriminatory and fair regarding sensitive features such as race, gender, religion, and so on. Several researchers have recently begun to develop fairness-aware or discrimination-aware data mining techniques that take into account issues of social fairness, discrimination, and neutrality. In this paper, after demonstrating the applications of these techniques, we explore the formal concepts of fairness and techniques for handling fairness in data mining. We then provide an integrated view of these concepts based on statistical independence. Finally, we discuss the relations between fairness-aware data mining and other research topics, such as privacy-preserving data mining or causal inference.
Fairness-aware Classifier with Prejudice Remover Regularizer
Fairness-aware Classifier with Prejudice Remover Regularizer
Proceedings of the European Conference on Machine Learning and Principles of Knowledge Discovery in Databases (ECMLPKDD), Part II, pp.35-50 (2012)
Article @ Official Site: http://dx.doi.org/10.1007/978-3-642-33486-3_3
Article @ Personal Site: http://www.kamishima.net/archive/2012-p-ecmlpkdd-print.pdf
Handnote: http://www.kamishima.net/archive/2012-p-ecmlpkdd-HN.pdf
Program codes : http://www.kamishima.net/fadm/
Conference Homepage: http://www.ecmlpkdd2012.net/
Abstract:
With the spread of data mining technologies and the accumulation of social data, such technologies and data are being used for determinations that seriously affect individuals' lives. For example, credit scoring is frequently determined based on the records of past credit data together with statistical prediction techniques. Needless to say, such determinations must be nondiscriminatory and fair in sensitive features, such as race, gender, religion, and so on. Several researchers have recently begun to attempt the development of analysis techniques that are aware of social fairness or discrimination. They have shown that simply avoiding the use of sensitive features is insufficient for eliminating biases in determinations, due to the indirect influence of sensitive information. In this paper, we first discuss three causes of unfairness in machine learning. We then propose a regularization approach that is applicable to any prediction algorithm with probabilistic discriminative models. We further apply this approach to logistic regression and empirically show its effectiveness and efficiency.
Enhancement of the Neutrality in Recommendation
Enhancement of the Neutrality in Recommendation
Workshop on Human Decision Making in Recommender Systems, in conjunction with RecSys 2012
Article @ Official Site: http://ceur-ws.org/Vol-893/
Article @ Personal Site: http://www.kamishima.net/archive/2012-ws-recsys-print.pdf
Handnote : http://www.kamishima.net/archive/2012-ws-recsys-HN.pdf
Program codes : http://www.kamishima.net/inrs
Workshop Homepage: http://recex.ist.tugraz.at/RecSysWorkshop2012
Abstract:
This paper proposes an algorithm for making recommendation so that the neutrality toward the viewpoint specified by a user is enhanced. This algorithm is useful for avoiding to make decisions based on biased information. Such a problem is pointed out as the filter bubble, which is the influence in social decisions biased by a personalization technology. To provide such a recommendation, we assume that a user specifies a viewpoint toward which the user want to enforce the neutrality, because recommendation that is neutral from any information is no longer recommendation. Given such a target viewpoint, we implemented information neutral recommendation algorithm by introducing a penalty term to enforce the statistical independence between the target viewpoint and a preference score. We empirically show that our algorithm enhances the independence toward the specified viewpoint by and then demonstrate how sets of recommended items are changed.
OpenOpt の線形計画で圧縮センシング
圧縮センシングの実装を具体例に，最適化エンジンのpython用フロントエンドである OpenOpt と，その拡張パッケージである FuncDesigner の使い方を紹介します．
Tokyo.Scipy #3 http://partake.in/events/ac0fcc7d-a289-4e2a-bb8e-1965aab8b17b
Sample Code : https://github.com/tokyo-scipy/archive/tree/master/003/shima__shima
Fairness-aware Learning through Regularization Approach
Fairness-aware Learning through Regularization Approach
The 3rd IEEE International Workshop on Privacy Aspects of Data Mining (PADM 2011)
Dec. 11, 2011 @ Vancouver, Canada, in conjunction with ICDM2011
Article @ Official Site: http://doi.ieeecomputersociety.org/10.1109/ICDMW.2011.83
Article @ Personal Site: http://www.kamishima.net/archive/2011-ws-icdm_padm.pdf
Handnote: http://www.kamishima.net/archive/2011-ws-icdm_padm-HN.pdf
Workshop Homepage: http://www.zurich.ibm.com/padm2011/
Abstract:
With the spread of data mining technologies and the accumulation of social data, such technologies and data are being used for determinations that seriously affect people's lives. For example, credit scoring is frequently determined based on the records of past credit data together with statistical prediction techniques. Needless to say, such determinations must be socially and legally fair from a viewpoint of social responsibility; namely, it must be unbiased and nondiscriminatory in sensitive features, such as race, gender, religion, and so on. Several researchers have recently begun to attempt the development of analysis techniques that are aware of social fairness or discrimination. They have shown that simply avoiding the use of sensitive features is insufficient for eliminating biases in determinations, due to the indirect influence of sensitive information. From a privacy-preserving viewpoint, this can be interpreted as hiding sensitive information when classification results are observed. In this paper, we first discuss three causes of unfairness in machine learning. We then propose a regularization approach that is applicable to any prediction algorithm with probabilistic discriminative models. We further apply this approach to logistic regression and empirically show its effectiveness and efficiency.
Pythonによる機械学習実験の管理
Python をつかったアルゴリズムの実験で，実験条件の設定や，その記録など実験管理，epydoc を用いたソフトの文書化，そして gxp を使ったクラスタ環境下での実行について簡単に紹介します．
Tokyo.Scipy #1 http://partake.in/events/3ab83ec0-e987-4956-b726-faf7777a2c83
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Future Directions of Fairness-Aware Data Mining: Recommendation, Causality, a...
Future Directions of Fairness-Aware Data Mining: Recommendation, Causality, and Theoretical Aspects
Invited Talk @ Workshop on Fairness, Accountability, and Transparency in Machine Learning
In conjunction with the ICML 2015 @ Lille, France, Jul. 11, 2015
Web Site: http://www.kamishima.net/fadm/
Handnote: http://www.kamishima.net/archive/2015-ws-icml-HN.pdf
The goal of fairness-aware data mining (FADM) is to analyze data while taking into account potential issues of fairness. In this talk, we will cover three topics in FADM:
1. Fairness in a Recommendation Context: In classification tasks, the term "fairness" is regarded as anti-discrimination. We will present other types of problems related to the fairness in a recommendation context.
2. What is Fairness: Most formal definitions of fairness have a connection with the notion of statistical independence. We will explore other types of formal fairness based on causality, agreement, and unfairness.
3. Theoretical Problems of FADM: After reviewing technical and theoretical open problems in the FADM literature, we will introduce the theory of the generalization bound in terms of accuracy as well as fairness.
Joint work with Jun Sakuma, Shotaro Akaho, and Hideki Asoh
Correcting Popularity Bias by Enhancing Recommendation Neutrality
Correcting Popularity Bias by Enhancing Recommendation Neutrality on
The 8th ACM Conference on Recommender Systems, Poster
Article @ Official Site: http://ceur-ws.org/Vol-1247/
Article @ Personal Site: http://www.kamishima.net/archive/2014-po-recsys-print.pdf
Abstract:
In this paper, we attempt to correct a popularity bias, which is the tendency for popular items to be recommended more frequently, by enhancing recommendation neutrality. Recommendation neutrality involves excluding specified information from the prediction process of recommendation. This neutrality was formalized as the statistical independence between a recommendation result and the specified information, and we developed a recommendation algorithm that satisfies this independence constraint. We correct the popularity bias by enhancing neutrality with respect to information regarding whether candidate items are popular or not. We empirically show that a popularity bias in the predicted preference scores can be corrected.
PyMCがあれば，ベイズ推定でもう泣いたりなんかしない
ベイズ推定の基本とPyMCによる簡単な実装例です．
関連資料： https://github.com/scipy-japan/tokyo-scipy/tree/master/006/shima__shima
The Independence of Fairness-aware Classifiers
The Independence of Fairness-aware Classifiers
IEEE International Workshop on Privacy Aspects of Data Mining (PADM), in conjunction with ICDM2013
Article @ Official Site:
Article @ Personal Site: http://www.kamishima.net/archive/2013-ws-icdm-print.pdf
Handnote : http://www.kamishima.net/archive/2013-ws-icdm-HN.pdf
Program codes : http://www.kamishima.net/fadm/
Workshop Homepage: http://www.cs.cf.ac.uk/padm2013/
Abstract:
Due to the spread of data mining technologies, such technologies are being used for determinations that seriously affect individuals' lives. For example, credit scoring is frequently determined based on the records of past credit data together with statistical prediction techniques. Needless to say, such determinations must be nondiscriminatory and fair in sensitive features, such as race, gender, religion, and so on. The goal of fairness-aware classifiers is to classify data while taking into account the potential issues of fairness, discrimination, neutrality, and/or independence. In this paper, after reviewing fairness-aware classification methods, we focus on one such method, Calders and Verwer's two-naive-Bayes method. This method has been shown superior to the other classifiers in terms of fairness, which is formalized as the statistical independence between a class and a sensitive feature. However, the cause of the superiority is unclear, because it utilizes a somewhat heuristic post-processing technique rather than an explicitly formalized model. We clarify the cause by comparing this method with an alternative naive Bayes classifier, which is modified by a modeling technique called "hypothetical fair-factorization." This investigation reveals the theoretical background of the two-naive-Bayes method and its connections with other methods. Based on these findings, we develop another naive Bayes method with an "actual fair-factorization" technique and empirically show that this new method can achieve an equal level of fairness as that of the two-naive-Bayes classifier.
Efficiency Improvement of Neutrality-Enhanced Recommendation
Efficiency Improvement of Neutrality-Enhanced Recommendation
Workshop on Human Decision Making in Recommender Systems, in conjunction with RecSys 2013
Article @ Official Site: http://ceur-ws.org/Vol-1050/
Article @ Personal Site: http://www.kamishima.net/archive/2013-ws-recsys-print.pdf
Handnote : http://www.kamishima.net/archive/2013-ws-recsys-HN.pdf
Program codes : http://www.kamishima.net/inrs/
Workshop Homepage: http://recex.ist.tugraz.at/RecSysWorkshop/
Abstract:
This paper proposes an algorithm for making recommendations so that neutrality from a viewpoint specified by the user is enhanced. This algorithm is useful for avoiding decisions based on biased information. Such a problem is pointed out as the filter bubble, which is the influence in social decisions biased by personalization technologies. To provide a neutrality-enhanced recommendation, we must first assume that a user can specify a particular viewpoint from which the neutrality can be applied, because a recommendation that is neutral from all viewpoints is no longer a recommendation. Given such a target viewpoint, we implement an information-neutral recommendation algorithm by introducing a penalty term to enforce statistical independence between the target viewpoint and a rating. We empirically show that our algorithm enhances the independence from the specified viewpoint.
Absolute and Relative Clustering
Absolute and Relative Clustering
4th MultiClust Workshop on Multiple Clusterings, Multi-view Data, and Multi-source Knowledge-driven Clustering (Multiclust 2013)
Aug. 11, 2013 @ Chicago, U.S.A, in conjunction with KDD2013
Article @ Official Site: http://dx.doi.org/10.1145/2501006.2501013
Article @ Personal Site: http://www.kamishima.net/archive/2013-ws-kdd-print.pdf
Handnote: http://www.kamishima.net/archive/2013-ws-kdd-HN.pdf
Workshop Homepage: http://cs.au.dk/research/research-areas/data-intensive-systems/projects/multiclust2013/
Abstract:
Research into (semi-)supervised clustering has been increasing. Supervised clustering aims to group similar data that are partially guided by the user's supervision. In this supervised clustering, there are many choices for formalization. For example, as a type of supervision, one can adopt labels of data points, must/cannot links, and so on. Given a real clustering task, such as grouping documents or image segmentation, users must confront the question ``How should we mathematically formalize our task?''To help answer this question, we propose the classification of real clusterings into absolute and relative clusterings, which are defined based on the relationship between the resultant partition and the data set to be clustered. This categorization can be exploited to choose a type of task formalization.
Consideration on Fairness-aware Data Mining
Consideration on Fairness-aware Data Mining
IEEE International Workshop on Discrimination and Privacy-Aware Data Mining (DPADM 2012)
Dec. 10, 2012 @ Brussels, Belgium, in conjunction with ICDM2012
Article @ Official Site: http://doi.ieeecomputersociety.org/10.1109/ICDMW.2012.101
Article @ Personal Site: http://www.kamishima.net/archive/2012-ws-icdm-print.pdf
Handnote: http://www.kamishima.net/archive/2012-ws-icdm-HN.pdf
Workshop Homepage: https://sites.google.com/site/dpadm2012/
Abstract:
With the spread of data mining technologies and the accumulation of social data, such technologies and data are being used for determinations that seriously affect individuals' lives. For example, credit scoring is frequently determined based on the records of past credit data together with statistical prediction techniques. Needless to say, such determinations must be nondiscriminatory and fair regarding sensitive features such as race, gender, religion, and so on. Several researchers have recently begun to develop fairness-aware or discrimination-aware data mining techniques that take into account issues of social fairness, discrimination, and neutrality. In this paper, after demonstrating the applications of these techniques, we explore the formal concepts of fairness and techniques for handling fairness in data mining. We then provide an integrated view of these concepts based on statistical independence. Finally, we discuss the relations between fairness-aware data mining and other research topics, such as privacy-preserving data mining or causal inference.
Fairness-aware Classifier with Prejudice Remover Regularizer
Fairness-aware Classifier with Prejudice Remover Regularizer
Proceedings of the European Conference on Machine Learning and Principles of Knowledge Discovery in Databases (ECMLPKDD), Part II, pp.35-50 (2012)
Article @ Official Site: http://dx.doi.org/10.1007/978-3-642-33486-3_3
Article @ Personal Site: http://www.kamishima.net/archive/2012-p-ecmlpkdd-print.pdf
Handnote: http://www.kamishima.net/archive/2012-p-ecmlpkdd-HN.pdf
Program codes : http://www.kamishima.net/fadm/
Conference Homepage: http://www.ecmlpkdd2012.net/
Abstract:
With the spread of data mining technologies and the accumulation of social data, such technologies and data are being used for determinations that seriously affect individuals' lives. For example, credit scoring is frequently determined based on the records of past credit data together with statistical prediction techniques. Needless to say, such determinations must be nondiscriminatory and fair in sensitive features, such as race, gender, religion, and so on. Several researchers have recently begun to attempt the development of analysis techniques that are aware of social fairness or discrimination. They have shown that simply avoiding the use of sensitive features is insufficient for eliminating biases in determinations, due to the indirect influence of sensitive information. In this paper, we first discuss three causes of unfairness in machine learning. We then propose a regularization approach that is applicable to any prediction algorithm with probabilistic discriminative models. We further apply this approach to logistic regression and empirically show its effectiveness and efficiency.
Enhancement of the Neutrality in Recommendation
Enhancement of the Neutrality in Recommendation
Workshop on Human Decision Making in Recommender Systems, in conjunction with RecSys 2012
Article @ Official Site: http://ceur-ws.org/Vol-893/
Article @ Personal Site: http://www.kamishima.net/archive/2012-ws-recsys-print.pdf
Handnote : http://www.kamishima.net/archive/2012-ws-recsys-HN.pdf
Program codes : http://www.kamishima.net/inrs
Workshop Homepage: http://recex.ist.tugraz.at/RecSysWorkshop2012
Abstract:
This paper proposes an algorithm for making recommendation so that the neutrality toward the viewpoint specified by a user is enhanced. This algorithm is useful for avoiding to make decisions based on biased information. Such a problem is pointed out as the filter bubble, which is the influence in social decisions biased by a personalization technology. To provide such a recommendation, we assume that a user specifies a viewpoint toward which the user want to enforce the neutrality, because recommendation that is neutral from any information is no longer recommendation. Given such a target viewpoint, we implemented information neutral recommendation algorithm by introducing a penalty term to enforce the statistical independence between the target viewpoint and a preference score. We empirically show that our algorithm enhances the independence toward the specified viewpoint by and then demonstrate how sets of recommended items are changed.
OpenOpt の線形計画で圧縮センシング
圧縮センシングの実装を具体例に，最適化エンジンのpython用フロントエンドである OpenOpt と，その拡張パッケージである FuncDesigner の使い方を紹介します．
Tokyo.Scipy #3 http://partake.in/events/ac0fcc7d-a289-4e2a-bb8e-1965aab8b17b
Sample Code : https://github.com/tokyo-scipy/archive/tree/master/003/shima__shima
Fairness-aware Learning through Regularization Approach
Fairness-aware Learning through Regularization Approach
The 3rd IEEE International Workshop on Privacy Aspects of Data Mining (PADM 2011)
Dec. 11, 2011 @ Vancouver, Canada, in conjunction with ICDM2011
Article @ Official Site: http://doi.ieeecomputersociety.org/10.1109/ICDMW.2011.83
Article @ Personal Site: http://www.kamishima.net/archive/2011-ws-icdm_padm.pdf
Handnote: http://www.kamishima.net/archive/2011-ws-icdm_padm-HN.pdf
Workshop Homepage: http://www.zurich.ibm.com/padm2011/
Abstract:
With the spread of data mining technologies and the accumulation of social data, such technologies and data are being used for determinations that seriously affect people's lives. For example, credit scoring is frequently determined based on the records of past credit data together with statistical prediction techniques. Needless to say, such determinations must be socially and legally fair from a viewpoint of social responsibility; namely, it must be unbiased and nondiscriminatory in sensitive features, such as race, gender, religion, and so on. Several researchers have recently begun to attempt the development of analysis techniques that are aware of social fairness or discrimination. They have shown that simply avoiding the use of sensitive features is insufficient for eliminating biases in determinations, due to the indirect influence of sensitive information. From a privacy-preserving viewpoint, this can be interpreted as hiding sensitive information when classification results are observed. In this paper, we first discuss three causes of unfairness in machine learning. We then propose a regularization approach that is applicable to any prediction algorithm with probabilistic discriminative models. We further apply this approach to logistic regression and empirically show its effectiveness and efficiency.
Pythonによる機械学習実験の管理
Python をつかったアルゴリズムの実験で，実験条件の設定や，その記録など実験管理，epydoc を用いたソフトの文書化，そして gxp を使ったクラスタ環境下での実行について簡単に紹介します．
Tokyo.Scipy #1 http://partake.in/events/3ab83ec0-e987-4956-b726-faf7777a2c83
More from Toshihiro Kamishima (13)
Future Directions of Fairness-Aware Data Mining: Recommendation, Causality, a...
Future Directions of Fairness-Aware Data Mining: Recommendation, Causality, a...
Correcting Popularity Bias by Enhancing Recommendation Neutrality
Correcting Popularity Bias by Enhancing Recommendation Neutrality
Efficiency Improvement of Neutrality-Enhanced Recommendation
Efficiency Improvement of Neutrality-Enhanced Recommendation
Fairness-aware Classifier with Prejudice Remover Regularizer
Fairness-aware Classifier with Prejudice Remover Regularizer
Fairness-aware Learning through Regularization Approach
Fairness-aware Learning through Regularization Approach
WSDM2018読み会 資料
- 1. Offline A/B Testing for Recommender Systems A. Gilotte, C. Calauzénes, T. Nedelec, A. Abraham, and S. Dollé WSDM2018読み会，2018-04-14 担当：神嶌 敏弘 1
- 2. 概要 2 オンラインでは，現状 prod（対照群）と施策 test（実験群）を実 際に行って比較する これを低コストな prod のデータだけで低コストで実施したい BIS：施策で生じる分布の差で重み付けした重点サンプリング 不偏推定量だが，バリアンスが大きすぎる CIS：施策の効果が大きすぎる部分の影響をカットして分散を抑制 バリアンスは大きく減少するが，バイアスが生じる NCIS：バイアスを大域的に推定して補正 バイアスはちょっと補正できた PieceNCIS，PointNCIS：施策への入力に応じて局所的に補正する 提案手法 バイアスはとても減った
- 3. 準備 3 X：状況，特徴ベクトル，広告を出すサイトや閲覧者の特徴を示す A：行動 ̶ 推薦結果・表示する広告，定義域は大きい R：報酬，クリック率など 方策 πp (prod) と πt (test) は π(A | X) の確率分布 オンラインテスト：ΔR = Eπp[R] − Eπt[R] の効果を調べる 期待値はそれぞれの方策下で集めた標本から推定する オフラインテスト：方策 p の下での標本 Sp のみが与えられる ΔR̂ を推定するのに Eπt[R] をどうにか Sp から推定する必要 バイアスとバリアンス：いろいろな標本から推定した推定量の，母 集団の値に対する偏りとばらつき
- 4. Basic Importance Sampling 4 πt や πp は，自分で設計したものだから計算できる バイアスは 0 の不偏推定量 方策 p と t の乖離が大きいとバリアンスは大きくなる バリアンスが大きいと，本当に p と t で差があるかどうか不明に → このバリアンスを小さくしたい ˆRIS (πt ) = 1 n X (x,a,r )2Sn w(a,x)r where w(a,x) = πt (a|x) πp (a|x) 報酬重み 標本の大きさ 方策 p での標本
- 5. Doubly Robust Estimator 5 方策 t での報酬の良い推定量があるとき，観測値との差だけを重点 サンプリングすることでバリアンスを減らす 推薦の行動 A の定義域は大きいので，よい推定量を構成するのはほ ぼ無理 元々の R のバリアンスが大きいと，推定量との相関はどうやっても 小さい（Rがスパースな場合など） ˆRDR (πt ) = X (x,a,r )2Sn (r − ¯r (a,x)) w(a,x) + Eπt [¯r (A,X )|X = x] ! 方策 t での報酬の良い推定量
- 6. Normalized Importance Sampling 6 重み W の方策 p での期待値は1だが，有限の大きさの標本ではそう はならない 不偏推定量にはならないが，バリアンスはBISより小さい 少なくとも漸近的には不偏性をあきらめないとバリアンスを小さく するのは無理 [ ] ˆRNIS (πt ) = 1 P (x,a,r )2Sn w(a,x) X (x,a,r )2Sn w(a,x)r n ではなく標本から求める
- 7. Capped Importance Sampling 7 caping：しきい値 c と比較して大きすぎる重みを抑制 バリアンスは小さくなるが，バイアスを生じる cap した重みは以後 w̄(a, x) を記す ˆRmaxCIS (πt ,c) = 1 n X (x,a,r )2Sn min(w(a,x),c)r ˆRzeroCIS (πt ,c) = 1 n X (x,a,r )2Sn 1w(a,x)<cw(a,x)r 重みはたかだか c c 以上の重みは無視
- 8. CISのバイアス 8 cap されるときの報酬の期待値が小さくないとバイアスは大きい → 方策 test で高頻度になるとき報酬は小さい → そんな方策は悪い方策… よいバイアス・バリアンスのバランスをとれる c は存在しない 図2：1%ほどの改善を検出しようとしたときの水準とくらべて， バイアス・バリアンスが共にこの水準を下回ることはない Eπt [R] = Eπp [ ˆRCIS (πt ,c)] | {z } RCIS (πt,c) + Eπt 266664 R W −W W ������ W > c 377775 Pπt (W > c) | {z } BCIS (πt,c)
- 9. Normalized Capped Importance Sampling 9 NISとCISの組み合わせ capしていない部分の推定量を，capした部分に比例配分している 大域的なバイアスのモデルを使って，バイアスを補正したことに該 当 バリアンスをさらに小さくするには，入力 X に依存した補正が必要 （細かい議論は理解できなかった…） ˆRNCIS (πt ,c) = 1 n P (x,a,r )2Sn w(a,x)r 1 n P (x,a,r )2Sn w(a,x) Eπt W R W � Eπt W W � =漸近的にこの値になる →
- 10. Piecewise NCIS 10 X をグループに分けて，それぞれで推定する うまいグループ分けを見つけなければならない x∈X の報酬値が似ているグループを見つけるとよい Eπt [R] = X д2G Eπt [R|X 2 д]P(X 2 д) X のグループ グループ内でNCIS
- 11. Pointwise NCIS 11 さらに局所化して，x ごとに推定 ある x での報酬の期待値を計算するのは困難 E[W̄ /W | x] は，πt からのサンプリングで計算可能 Eπt [R] = X x 2X Eπt [R|X = x]P(X = x) X のそれぞれの値 同じ x のでの期待値 Eπt [R|X = x] ⇡ Eπt RW W ���� X = x � Eπt W W ���� X = x � ˆRPointNCIS (πt ,c) = 1 n X (x,a,r )2Sn ˆIPc (x)w(a,x)r