600 citations · 1.7k across the 29 of their papers we have counts for
16 papers · 1 filter
Zero-shot Item-based Recommendation via Multi-task Product Knowledge Graph Pre-Training
Ziwei Fan, Zhiwei Liu, Shelby Heinecke +4
Existing recommender systems face difficulties with zero-shot items, i.e. items that have no historical interactions with users during the training stage. Though recent works extra…
Towards More Robust and Accurate Sequential Recommendation with Cascade-guided Adversarial Training
Juntao Tan, Shelby Heinecke, Zhiwei Liu +3
Sequential recommendation models, models that learn from chronological user-item interactions, outperform traditional recommendation models in many settings. Despite the success of…
Explanation Guided Contrastive Learning for Sequential Recommendation
Lei Wang, Ee-Peng Lim, Zhiwei Liu +1
Recently, contrastive learning has been applied to the sequential recommendation task to address data sparsity caused by users with few item interactions and items with few user ad…
ContrastVAE: Contrastive Variational AutoEncoder for Sequential Recommendation
Yu Wang, Hengrui Zhang, Zhiwei Liu +2
Aiming at exploiting the rich information in user behaviour sequences, sequential recommendation has been widely adopted in real-world recommender systems. However, current methods…
Generating Negative Samples for Sequential Recommendation
Yongjun Chen, Jia Li, Zhiwei Liu +4
To make Sequential Recommendation (SR) successful, recent works focus on designing effective sequential encoders, fusing side information, and mining extra positive self-supervisio…
Large-scale Personalized Video Game Recommendation via Social-aware Contextualized Graph Neural Network
Liangwei Yang, Zhiwei Liu, Yu Wang +3
Because of the large number of online games available nowadays, online game recommender systems are necessary for users and online game platforms. The former can discover more pote…
Sequential Recommendation via Stochastic Self-Attention
Ziwei Fan, Zhiwei Liu, Alice Wang +4
Sequential recommendation models the dynamics of a user's previous behaviors in order to forecast the next item, and has drawn a lot of attention. Transformer-based approaches, whi…
Pre-training Recommender Systems via Reinforced Attentive Multi-relational Graph Neural Network
Xiaohan Li, Zhiwei Liu, Stephen Guo +4
Recently, Graph Neural Networks (GNNs) have proven their effectiveness for recommender systems. Existing studies have applied GNNs to capture collaborative relations in the data. H…
Federated Social Recommendation with Graph Neural Network
Zhiwei Liu, Liangwei Yang, Ziwei Fan +2
Recommender systems have become prosperous nowadays, designed to predict users' potential interests in items by learning embeddings. Recent developments of the Graph Neural Network…
Pre-training Graph Neural Network for Cross Domain Recommendation
Chen Wang, Yueqing Liang, Zhiwei Liu +2
A recommender system predicts users' potential interests in items, where the core is to learn user/item embeddings. Nevertheless, it suffers from the data-sparsity issue, which the…
Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer
Ziwei Fan, Zhiwei Liu, Jiawei Zhang +3
In order to model the evolution of user preference, we should learn user/item embeddings based on time-ordered item purchasing sequences, which is defined as Sequential Recommendat…
Contrastive Self-supervised Sequential Recommendation with Robust Augmentation
Zhiwei Liu, Yongjun Chen, Jia Li +3
Sequential Recommendationdescribes a set of techniques to model dynamic user behavior in order to predict future interactions in sequential user data. At their core, such approache…
Modeling Sequences as Distributions with Uncertainty for Sequential Recommendation
Ziwei Fan, Zhiwei Liu, Lei Zheng +2
The sequential patterns within the user interactions are pivotal for representing the user's preference and capturing latent relationships among items. The recent advancements of s…
Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training Transformer
Zhiwei Liu, Ziwei Fan, Yu Wang +1
Sequential Recommendation characterizes the evolving patterns by modeling item sequences chronologically. The essential target of it is to capture the item transition correlations.…
Deoscillated Graph Collaborative Filtering
Zhiwei Liu, Lin Meng, Fei Jiang +2
Collaborative Filtering (CF) signals are crucial for a Recommender System~(RS) model to learn user and item embeddings. High-order information can alleviate the cold-start issue of…
Basket Recommendation with Multi-Intent Translation Graph Neural Network
Zhiwei Liu, Xiaohan Li, Ziwei Fan +3
The problem of basket recommendation~(BR) is to recommend a ranking list of items to the current basket. Existing methods solve this problem by assuming the items within the same b…