Xiting Wang

Fairness-aware News Recommendation with Decomposed Adversarial Learning

2021

News recommendation is important for online news services. Existing news recommendation models are usually learned from users' news click behaviors. Usually the behaviors of users with the same sensitive attributes (e.g., genders) have similar patterns …

Multi-level Recommendation Reasoning over Knowledge Graphs with Reinforcement Learning

2022 · Proceedings of the ACM Web Conference 2022

Knowledge graphs (KGs) have been widely used to improve recommendation accuracy. The multi-hop paths on KGs also enable recommendation reasoning, which is considered a crystal type of explainability. In this paper, we propose a reinforcement …

Towards Explainable Collaborative Filtering with Taste Clusters Learning

2023

Collaborative Filtering (CF) is a widely used and effective technique for recommender systems. In recent decades, there have been significant advancements in latent embedding-based CF methods for improved accuracy, such as matrix factorization, neural collaborative …

Neural Recommendation Reasoning with Logic Rules

2025 · ACM Transactions on Information Systems

Explainability is critical for recommender systems to ensure good user experience and facilitate designers to debug. However, generating explanations in recommender systems usually requires large efforts due to the dependency on additional data and case-by-case …

Learning to Detect Unseen Jailbreak Attacks in Large Vision-Language Models

2025 · arXiv (Cornell University)

Despite extensive alignment efforts, Large Vision-Language Models (LVLMs) remain vulnerable to jailbreak attacks. To mitigate these risks, existing detection methods are essential, yet they face two major challenges: generalization and accuracy. While learning-based methods trained …

Explainable Recommendation through Attentive Multi-View Learning

2019 · Proceedings of the AAAI Conference on Artificial Intelligence

Recommender systems have been playing an increasingly important role in our daily life due to the explosive growth of information. Accuracy and explainability are two core aspects when we evaluate a recommendation model and have …

Co-Attentive Multi-Task Learning for Explainable Recommendation

2019

Despite widespread adoption, recommender systems remain mostly black boxes. Recently, providing explanations about why items are recommended has attracted increasing attention due to its capability to enhance user trust and satisfaction. In this paper, we …

A Neural Influence Diffusion Model for Social Recommendation

2019

Precise user and item embedding learning is the key to building a successful recommender system. Traditionally, Collaborative Filtering (CF) provides a way to learn user and item embeddings from the user-item interaction history. However, the …

Leveraging Demonstrations for Reinforcement Recommendation Reasoning over Knowledge Graphs

2020

Knowledge graphs have been widely adopted to improve recommendation accuracy. The multi-hop user-item connections on knowledge graphs also endow reasoning about why an item is recommended. However, reasoning on paths is a complex combinatorial optimization …