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Cognitive diagnosis, a fundamental task in education assessments, aims to quantify the students' proficiency level based on the historical test logs. However, the interactions between students and exercises are incomplete and even sparse, which means ...

Research on consumers' acquisition and redemption behaviors of e-coupons can assist e-commerce enterprises in accurately placing e-coupons and improving marketing efficiency. This paper presents theoretical models for consumers' behavior of acquiring and ...

Unbiased learning to rank methods have been proposed to address biases in search ranking. These biases, known as position bias and sample selection bias, often occur simultaneously in real applications. Existing approaches either tackle these biases ...

Click-Through Rate (CTR) prediction serves as a fundamental component in online advertising. A common practice is to train a CTR model on advertisement (ad) impressions with user feedback. Since ad impressions are purposely selected by the model itself, ...

Conversion rate (CVR) prediction is an essential task for e-commerce platforms. However, refunds frequently occur after conversion in online shopping systems, which drives us to pay attention to effective conversion for building healthier services. This ...

Almost all existing causal feature selection methods are proposed without considering the problem of sample selection bias. However, in practice, as data-gathering process cannot be fully controlled, sample selection bias often occurs, leading to spurious ...

Recommender Systems (RS), as an efficient tool to discover users' interested items from a very large corpus, has attracted more and more attention from academia and industry. As the initial stage of RS, large-scale matching is fundamental yet ...

Target-oriented opinion words extraction (TOWE) is a subtask of aspect-based sentiment analysis (ABSA). Given a sentence and an aspect term occurring in the sentence, TOWE extracts the corresponding opinion words for the aspect term. TOWE has two types ...

Causal models are notoriously difficult to validate because they make untestable assumptions regarding confounding. New scientific experiments offer the possibility of evaluating causal models using prediction performance. Prediction performance ...

To address the sample selection bias between the training and test data, previous research works focus on reweighing biased training data to match the test data and then building classification models on the reweighed training data. However, how to ...

Imbalanced data sets are a common occurrence in important machine learning problems. Research in improving learning under imbalanced conditions has largely focused on classification problems (ie, problems with a categorical dependent variable). ...

Estimating post-click conversion rate (CVR) accurately is crucial for ranking systems in industrial applications such as recommendation and advertising. Conventional CVR modeling applies popular deep learning methods and achieves state-of-the-art ...

Performance measurements obtained from dividing a single sample into training and test sets, e.g. by employing cross-validation, may not give an accurate picture of the performance of any model developed from the sample, on the set of examples to which ...

Importance of concern assessment has been increased in Japanese regional communities. We have developed an e-Participation web platform based on a Linked Open Data set called SOCIA (Social Opinions and Concerns for Ideal Argumentation). To sophisticate ...

Online auction has become a very popular e-commerce transaction type. The immense business opportunities attract a lot of individuals as well as online stores. With more sellers engaged in, the competition between sellers is more intense. For sellers, ...

The paper uses micro-data of a certain industry to estimate the gender wage discrimination of domestic labor markets. With the existence of sample selection bias in the micro- data used in this paper, traditional OLS estimators are biased, and we used ...

In this paper, we discuss an application of spatial data mining to predict pedestrian flow in extensive road networks using a large biased sample. Existing out-of-the-box techniques are not able to appropriately deal with its challenges and constraints, ...

This paper presents approaches to semi-supervised learning when the labeled training data and test data are differently distributed. Specifically, the samples selected for labeling are a biased subset of some general distribution and the test set ...

One of the most important assumptions made by many classification algorithms is that the training and test sets are drawn from the same distribution, i.e., the so-called "stationary distribution assumption" that the future and the past data sets are ...

Most learning methods assume that the training set is drawn randomly from the population to which the learned model is to be applied. However in many applications this assumption is invalid. For example, lending institutions create models of who is ...