Toshihiro Kamishima

Fairness-aware techniques are designed to remove socially sensitive information, such as gender or race. Many types of fairness-aware predictors have been developed, but they were designed essentially to improve the accuracy or fairness of the prediction results. We focus herein on another aspect of fairness-aware predictors, i.e., the stability. W...

Recommender systems provide personalized information access to users of Internet services from social networks to e-commerce to media and entertainment. As is appropriate for research in a field with a focus on personalization, academic studies of recommender systems have largely concentrated on optimizing for user experience when designing, implem...

The purpose of the SIGIR 2019 workshop on Fairness, Accountability, Confidentiality, Transparency , and Safety (FACTS-IR) was to explore challenges in responsible information retrieval system development and deployment. To this end, the workshop aimed to crowd-source from the larger SIGIR community and draft an actionable research agenda on five ke...

Recommender systems are personalized information access applications; they are ubiquitous in today's online environment, and effective at finding items that meet user needs and tastes. As the reach of recommender systems has extended, it has become apparent that the single-minded focus on the user common to academic research has obscured other impo...

The second Workshop on Responsible Recommendation (FATREC 2018) was held in conjunction with the 12th ACM Conference on Recommender Systems on October 6th, 2018 in Vancouver, Canada. This full-day workshop brought together researchers and practitioners to discuss several topics under the banner of social responsibility in recommender systems: fairn...

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 woul...

The goal of fairness-aware classification is to categorize data while taking into account potential issues of fairness, discrimination, neutrality, and/or independence. For example, when applying data mining technologies to university admissions, admission criteria must be non-discriminatory and fair with regard to sensitive features, such as gende...

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 recommend...

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...

Object ranking is a problem that involves ordering given objects by aggregating pairwise comparison data collected from one or more evaluators; however, the cost of object evaluations is high in some applications. In this paper, we propose an efficient data collection method called progressive comparison, whose objective is to collect many pairwise...

With recent developments in machine learning technology, the predictions by systems incorporating machine learning can now have a significant impact on the lives and activities of individuals. In some cases, predictions made by machine learning can result unexpectedly in unfair treatments to individuals. For example, if the results are highly depen...

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 argue three topics of FADM: 1. Fairness in a Recommendation Context: In a case of classification task, the term ”fairness” is regarded as anti-discrimination. We will show other types of problems related to...

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 be...

Crowdsourcing is a promising solution to problems that are difficult for computers, but relatively easy for humans. One of the biggest challenges in crowdsourcing is quality control, since high quality results cannot be expected from crowdworkers who are not necessarily very capable or motivated. Several statistical crowdsourcing quality control me...

Data mining of clinical data that are stored continually in the course of daily medical practice will contribute to the advancement of healthcare. However, real-world clinical data are characteristically noisy, sparse, irregular, and biased, which makes it difficult to perform data mining. This study assesses an exploratory approach to ascertain ho...

One of the biggest challenges in crowdsourcing is quality control which is to expect high quality results from crowd workers who are not necessarily very capable nor motivated.In this paper, we consider item ordering questions, where workersare asked to arrange multiple items in the correct order. We propose a probabilistic generative model of crow...

One of the biggest challenges in crowdsourcing is quality control which is to expect high quality results from crowd workers who are not necessarily very capable nor motivated. In this paper, we consider item ordering questions, where workersare asked to arrange multiple items in the correct order. We propose a probabilistic generative model of cro...

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 prov...

With recent developments in machine learning technology, the resulting predictions can now have a significant impact on the lives and activities of individuals. In some cases, predictions made by machine learning can result unexpectedly in unfair treatments to individuals. For example, if the results are highly dependent on personal attributes, suc...

It is an important issue to utilize large amount of medical records which are being accumulated on medical information systems to improve the quality of medical treatment. The process of medical treatment can be considered as a sequential interaction process between doctors and patients. From this viewpoint, we have been modeling medical records us...

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...

Analyzing long-term medical records of patients suffering from chronic diseases is beginning to be recognized as an important issue in medical data analysis. Long term medical treatments can be considered as inter- actions between doctors and patients, and in the machine learning community, Markov decision processes (MDP) are commonly used to model...

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 d...

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 d...

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 inuence in social decisions biased by a personalization technolo...

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 deter...

Many e-commerce sites use recommender systems, which suggest items that customers prefer. Though recommender systems have achieved great success, their potential is not yet fulfilled. One weakness of current systems is that the actions of the system toward customers are restricted to simply showing items. We propose a system that relaxes this restr...

A recommender system has to collect users' preference data. To collect such data, rating or scoring methods that use rating scales, such as good-fair-poor or a five-point-scale, have been employed. We replaced such collection methods with a ranking method, in which objects are sorted according to the degree of a user's preference. We developed a te...

Ordered lists of objects are widely used as representational forms. Such ordered objects include Web search results or bestseller lists. In spite of their importance, methods of processing orders have received little attention. However, research concerning orders has recently become common; in particular, researchers have developed various methods...

Ordered lists of objects are widely used as representational forms. Such ordered objects include Web search results and best-seller lists. Techniques for processing such ordinal data are being developed, particularly methods for a supervised ordering task: i.e., learning func- tions used to sort objects from sample orders. In this article, we propo...

The aim of transfer learning is to improve prediction accuracy on a target task by exploiting the training examples for tasks that are related to the target one. Transfer learning has received more attention in recent years, because this technique is considered to be helpful in reducing the cost of labeling. In this paper, we propose a very simple...

Invited Talk about Object Ranking

Lists of ordered objects are widely used as representational forms. Such ordered objects include Web search results or best-seller lists. Clustering is a useful data analysis technique for grouping mutually similar objects. To cluster orders, hierarchical clustering methods have been used together with dissimilarities defined between pairs of order...

We address a new machine learning problem, taming, that involves two types of training sets: wild data and tame data. These two types of data sets are mutually complementary. A wild data set is less consistent, but is much larger in size than a tame set. Conversely, a tame set has consistency but not as many data. The goal of our taming task is to...

We proposed BaggTaming to boost the prediction accuracy by exploiting additional data whose class labels are less reliable. This algorithm is successfully applied to the personalized tag predicition for the data collected from the delicious. To check whether our method is generally effective, we test the data crawled from the hatena bookmark.

Lists of ordered objects are widely used as representational forms. Such ordered objects include Web search results or best-seller lists. Clustering is a useful data analysis technique for grouping mutually similar objects. To cluster orders, hierarchical clustering methods have been used together with dissimilarities defined between pairs of order...

Ordered lists of objects are widely used as representational forms. Such ordered objects include Web search results and best-seller lists. Techniques for processing such ordinal data are being developed, particularly methods for a supervised ordering task: i.e., learning functions used to sort objects from sample orders. In this article, we propose...

Recommender systems use collaborative filtering to recommend objects by summarizing the preference patterns of people who have similar patterns to the target user. Traditionally, these preference patterns are represented by rating scores. We developed recommendation methods using order responses instead of rating scores, and showed the advantages o...

In this paper, we advocate a learning task that deals with the orders of objects, which we call the Supervised Ordering task. The term order means a sequence of objects sorted according to a specific property, such as preference, size, cost. The aim of this task is to acquire the rule that is used for estimating an appropriate order of a given unor...

Ordered lists of objects are widely used as representational forms. Such ordered objects include Web search results or bestseller lists. In spite of their importance, methods of processing orders have received little attention. However, research concerning orders has become common; in particular, researchers have developed various methods for the t...

Filling-in techniques are important, since missing values frequently appear in real data. Such techniques have been established for categorical or numerical values. Though lists of ordered objects are widely used as representational forms (e.g., Web search results, best-seller lists), filling-in techniques for orders have received little attention....

Lists of ordered objects are widely used as representational forms. Such ordered objects include Web search results or best seller lists. In spite of their importance, the methods of processing orders have received little attention. However, research concerning object ordering is becoming more common. Some researchers have developed various methods...

We propose a method of using clustering techniques to partition a set of orders. We define the term order as a sequence of objects that are sorted according to some property, such as size, preference, or price. These orders are useful for, say, carrying out a sensory survey. We propose a method called the k-o'means method, which is a modified versi...

A recommender system suggests the items expected to be preferred by the users. Recommender systems use collaborative filtering to recommend items by summarizing the preferences of people who have tendencies similar to the user preference. Traditionally, the degree of preference is represented by a scale, for example, one that ranges from one to fiv...

Learning from cluster examples (LCE) is a hybrid task combining features of two common classification tasks: clustering and learning from examples. In LCE, each example is an object set with the true partition for the set, where the true partition is the one that users consider as the most appropriate for their aim among the possible partitions. Th...

We advocate a new learning task that deals with orders of items, and we call this the learning from order examples (LOE) task. The aim of the task is to acquire the rule that is used for estimating the proper order of a given unordered item set. The rule is acquired from training examples that are ordered item sets. We present several solution meth...

An experimental multimodal disputation system, Mr.Bengo, is a knowledge based system with multimodal user interfaces such as face recognition, face generation, speech recognition, speech generation and a WWW browser. Mr. Bengo deals with three agents - a prosecution, an defense attorney and the judge. The prosecution and the attorney dispute about...