Recommender systems have become crucial in information filtering nowadays. Existing
recommender systems extract user preferences based on the correlation in data, such as …

Fashion recommendation is a key research field in computational fashion research and has
attracted considerable interest in the computer vision, multimedia, and information retrieval …

Recommender systems (RS), serving at the forefront of Human-centered AI, are widely
deployed in almost every corner of the web and facilitate the human decision-making …

Offline reinforcement learning (RL), a technology that offline learns a policy from logged data
without the need to interact with online environments, has become a favorable choice in …

Recommender systems may be confounded by various types of confounding factors (also
called confounders) that may lead to inaccurate recommendations and sacrificed …

Causal inference has recently garnered significant interest among recommender system
(RS) researchers due to its ability to dissect cause-and-effect relationships and its broad …

Many of the traditional recommendation algorithms are designed based on the fundamental
idea of mining or learning correlative patterns from data to estimate the user-item correlative …

Recommender systems are important and powerful tools for various personalized services.
Traditionally, these systems use data mining and machine learning techniques to make …

In the era of information overload, recommender systems (RSs) have become an
indispensable part of online service platforms. Traditional RSs estimate user interests and …

Micro-video platforms such as TikTok are extremely popular nowadays. One important
feature is that users no longer select interested videos from a set; instead, they either watch …