In recent years, internet technologies and its rapid growth have created a paradigm of digital
services. In this new digital world, users suffer due to the information overload problem and …

In a recent study, Friedler et al. pointed out that several fair classification algorithms are not
stable with respect to variations in the training set–a crucial consideration in several …

Rapidly develo** academic venues throw a challenge to researchers in identifying the
most appropriate ones that are in-line with their scholarly interests and of high relevance …

Scholarly venue recommendation is an emerging field due to a rapid surge in the number of
scholarly venues concomitant with exponential growth in interdisciplinary research and …

The phenomenon of rapidly develo** academic venues poses a significant challenge for
researchers: how to recognize the ones that are not only in accordance with one's scholarly …

Manually selecting appropriate scholarly venues is becoming a tedious and time-consuming
task for researchers due to many reasons that include relevance, scientific impact, and …

In order to make a recommendation, a recommender system typically first predicts a user's
ratings for items and then recommends a list of items to the user which have high predicted …

In this paper, we study how to address the sparsity, accuracy and serendipity issues of top-N
recommendation with collaborative filtering (CF). Existing studies commonly use rated items …

Determining the right code reviewer for a given code change requires understanding the
characteristics of the changed code, identifying the skills of each potential reviewer …

Collaborative Filtering (CF) models lie at the core of most recommendation systems due to
their state-of-the-art accuracy. They are commonly adopted in e-commerce and online …