Research Topics

Fairness-Aware Data Mining

I am currently working on this research topic.
The goal of fairness-aware data mining is to analyze data while taking into account potential issues of fairness, discrimination, neutrality, and/or independence.
Studies related to fairness-aware data mining are summarized in the page: Fairness-Aware Data Mining

Learning for Rankings / Orders

The term order as a sequence of objects that are sorted according to some property, such as size, preference, or price. Lists of ordered objects are widely used as representational forms. Such ordered objects include Web search results or best seller lists. The analysis of orders is useful for sensory tests, information retrieval, or decision making.

  • SUSHI Preference Data Set: Questionnaire survey of preference in SUSHI collected by a ranking method. Test data for ordinal learning task.

Nantonac Collaborative Filtering

procedure of collaborative filtering
Procedure of collaborative filtering

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 (see right Figure). Traditional collaborative filtering algorithms adopted a scoring method (Figure (a)), in which preferences are measured using an n-point-scale, or a rating method, which employs scales such as good-fair-poor or gold-silver-bronze. We propose some collaborative filtering algorithms adopting a ranking method(Figure (b)). In this ranking method, the preferences are represented by orders, which are sorted item sequences according to the users’ preferences.

scoreing method
(a) Scoreing method
ranking method
(b) Ranking method

Publications: RecSys10, DMSS06, ICDM04, KDD03

Object Ranking (Supervised Ordering)

procedure of object ranking
Procedure of object ranking

Object Ranking is a task to learn a function for ranking objects from given sample orders. Training sample orders are sorted according to the degree of the target preference to learn. Objects in these orders are represented by feature vectors. From these samples, an object ranking method acquires a ranking function. By applying this learned function, unordered objects can be sorted according to the degree of the target preference. Note that objects that don’t appeared in training samples have to be ordered by referring feature vectors of objects.

Publications: Book10b, Book10a, PL@ECMLPKDD09, AIR06, ICDM06, ICDM05, ECML04, ICDM02

Clustering Rakings / Clustering Orders

A method of using clustering techniques to partition
a set of orders. k-o’means algorithm is a modified version of a k-means
adjusted to handle orders.

Publications: Book09, MCD@ICDM06, DS03

Supervised Clustering (Learning from Cluster Examples)

procedure of supervised clustering
Procedure of supervised clustering

Supervised Clustering is a hybrid task combining features of two common grouping tasks: supervised classification and and clustering. In supervised clustering, each training example is a partition of objects.The task is then to learn from a training set, a rule for partitioning unseen object sets. A general method for learning such partitioning rules is useful in any situation where explicit algorithms for deriving partitions are hard to formalize, while individual examples of correct partitions are easy to specify. In the past, clustering techniques have been applied to such problems, despite being essentially unsuited to the task.

Publications: KDDW13, ML03

Mr.Bengo: An Experimental Multimodal Disputation System

screen shot of Mr.Bengo
Screen shot of Mr.Bengo

An experimental multimodal disputation system, Mr.Bengo, is a knowledge based system with multimodal user interfaces composed of modules for face recognition, face animation, speech recognition, speech synthesis, and text interface. In a virtual court, an attorney agent interact with users and disputes with a prosecutor agent.

Publications: IJCAIW97, “Mr.Bengo” Homepage (in Japanese)