Zhiwei Liu
6 papers in the PaperMetrix corpus
Papers by this author
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JSCN: Joint Spectral Convolutional Network for Cross Domain Recommendation
2019 · arXiv (Cornell University)
Cross-domain recommendation can alleviate the data sparsity problem in recommender systems. To transfer the knowledge from one domain to another, one can either utilize the neighborhood information or learn a direct mapping function. However, all …
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CHEER: Centrality-aware High-order Event Reasoning Network for Document-level Event Causality Identification
2023
Document-level Event Causality Identification (DECI) aims to recognize causal relations between events within a document. Recent studies focus on building a document-level graph for cross-sentence reasoning, but ignore important causal structures — there are one …
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Personalized Multi-task Training for Recommender System
2024 · arXiv (Cornell University)
In the vast landscape of internet information, recommender systems (RecSys) have become essential for guiding users through a sea of choices aligned with their preferences. These systems have applications in diverse domains, such as news …
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Graph-Sequential Alignment and Uniformity: Toward Enhanced Recommendation Systems
2025
Graph-based and sequential methods are two popular recommendation paradigms, each excelling in its domain but lacking the ability to leverage signals from the other. To address this, we propose a novel method that integrates both …
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Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer
2021
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 Recommendation~(SR) problem. Existing methods leverage sequential patterns to model item …
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Intent Contrastive Learning for Sequential Recommendation
2022 · Proceedings of the ACM Web Conference 2022
Users’ interactions with items are driven by various intents (e.g., preparing for holiday gifts, shopping for fishing equipment, etc.). However, users’ underlying intents are often unobserved/latent, making it challenging to leverage such latent intents for …