Generative Adversarial Networks (GAN) have achieved big success in various domains
such as image generation, music generation, and natural language generation. In this …

User preference mining is an application of data mining that attracts increasing attention.
Although most of the existing user preference mining methods achieved significant …

Recommender systems aim to forecast users' rank, interests, and preferences in specific
products and recommend them to a user for purchase. Collaborative filtering is the most …

Recently, deep learning has become a preferred choice for performing tasks in diverse
application domains such as computer vision, natural language processing, sensor data …

Artificial intelligence models are increasingly used in manufacturing to inform decision
making. Responsible decision making requires accurate forecasts and an understanding of …

In this paper, we present a novel and publicly available dataset for online recommendation
provided by Purch1. The dataset records the clicks generated by users of one of Purch's …

The core objective of modelling recommender systems from implicit feedback is to maximize
the positive sample score sp and minimize the negative sample score sn, which can usually …

Purpose Online businesses require a deep understanding of their customers' interests to
innovate and develop new products and services. Users, on the other hand, rarely express …

Traditional statistical learning theory relies on the assumption that data are identically and
independently distributed (iid). However, this assumption often does not hold in many real …

Learning from human feedback is an effective way to improve robotic learning in exploration-
heavy tasks. Compared to the wide application of binary human feedback, scalar human …