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 …

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

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 …

User-generated data are extensively utilized in learning to rank as they are easy to collect
and up-to-date. However, the data inevitably contain noisy labels attributed to users' …

In the realm of recommender systems, the exploration of hyperbolic geometry-based
embeddings for users and items has emerged as a promising avenue, particularly in the …

Internet Protocol Television (IPTV) has had a significant impact on live TV content
consumption in the past decade, as improvements in the broadband speed have allowed …

Most existing denoising recommendation methods alleviate noisy implicit feedback (user
behaviors) through mainly empirical studies. However, such studies may lack theoretical …