Causal graph, as an effective and powerful tool for causal modeling, is usually assumed as
a Directed Acyclic Graph (DAG). However, recommender systems usually involve feedback …

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

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 …

Attention-based recommender models hold the promise of improving performance by
learning to discriminate different user/item feature importances. However, due to the …

Recommender Systems (RSs) are widely used to help online users discover products,
books, news, music, movies, courses, restaurants, etc. Because a traditional …

Collaborative filtering (CF) techniques recommend items to users based on their historical
ratings. In real-world scenarios, user interests may drift over time since they are affected by …

Two main challenges in recommender systems are modeling users with heterogeneous
taste, and providing explainable recommendations. In this paper, we propose the neural …

Auxiliary information, such as reviews, have been widely adopted to improve collaborative
filtering (CF) algorithms, eg, to boost the accuracy and provide explanations. However, most …

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

Over the past decades, for One-Class Collaborative Filtering (OCCF), many learning
objectives have been researched based on a variety of underlying probabilistic models …