Jianxun Lian

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 …

xDeepFM

2018

Combinatorial features are essential for the success of many commercial models. Manually crafting these features usually comes with high cost due to the variety, volume and velocity of raw data in web-scale systems. Factorization based …

Adaptive User Modeling with Long and Short-Term Preferences for Personalized Recommendation

2019

User modeling is an essential task for online recommender systems. In the past few decades, collaborative filtering (CF) techniques have been well studied to model users' long term preferences. Recently, recurrent neural networks (RNN) have …

MIND: A Large-scale Dataset for News Recommendation

2020

Fangzhao Wu, Ying Qiao, Jiun-Hung Chen, Chuhan Wu, Tao Qi, Jianxun Lian, Danyang Liu, Xing Xie, Jianfeng Gao, Winnie Wu, Ming Zhou. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020.

Self-supervised Graph Learning for Recommendation

2021

Representation learning on user-item graph for recommendation has evolved from using single ID or interaction history to exploiting higher-order neighbors. This leads to the success of graph convolution networks (GCNs) for recommendation such as PinSage …