In the original article, it was written that “Then MLE scoring and DIF analysis with RDIF statistics were performed using est_score rdif functions, respectively, in R (R Core Team, 2019) package irtplay (p.90).” However, has been removed from CRAN repository due to intellectual property (IP) violation issues. Instead, a new called irtQ (Lim & Wells, 2023) released as successor irtplay. All IP i...

Abstract New items, also called cold-start are introduced every day in the catalogs of numerous online systems. Due to absence previous preferences, recommending these items is difficult but important task for a recommender system. For this reason, item recommendation problem still represents an interesting research topic community. In work, we propose Neural Feature Combiner ( NFC ), novel dee...

R ecommendation algorithms are best known for their use on e-commerce Web sites,1 where they use input about a customer’s interests to generate a list of recommended items. Many applications use only the items that customers purchase and explicitly rate to represent their interests, but they can also use other attributes, including items viewed, demographic data, subject interests, and favorite...

Both reviews and user-item interactions (i.e., rating scores) have been widely adopted for user rating prediction. However, these existing techniques mainly extract the latent representations for users and items in an independent and static manner. That is, a single static feature vector is derived to encode her preference without considering the particular characteristics of each candidate ite...

Bayesian networks are commonly used in cognitive student modeling and assessment. They typically represent the item-concepts relationships, where items are observable responses to questions or exercises and concepts represent latent traits and skills. Bayesian networks can also represent concepts-concepts and concepts-misconceptions relationships. We explore their use for modeling item-item rel...