Fuzhen Zhuang

Tag correlation and user social relation based microblog recommendation

2016

A microblog recommendation method based on tag correlation and user social relation is proposed via analyzing microblog features and the deficiencies of existing microblog recommendation algorithm. Specifically, a tag retrieval strategy is established to add …

Follow the Title Then Read the Article: Click-Guide Network for Dwell Time Prediction

2019 · IEEE Transactions on Knowledge and Data Engineering

In article recommendation, the amount of time user spends on viewing articles, dwell time, is an important metric to measure the post-click engagement of user on content and has been widely used as a proxy …

A Survey on Knowledge Graph-Based Recommender Systems

2020 · arXiv (Cornell University)

To solve the information explosion problem and enhance user experience in various online applications, recommender systems have been developed to model users preferences. Although numerous efforts have been made toward more personalized recommendations, recommender systems …

Compressing Knowledge Graph Embedding with Relational Graph Auto-encoder

2020

Knowledge graphs (KGs) are extremely useful resources for varieties of applications. However, with the large and steadily growing sizes of modern KGs, knowledge graph embeddings (KGE), which represent entities and relations in KGs into 32-bit …

Transfer-Meta Framework for Cross-domain Recommendation to Cold-Start Users

2021

Cold-start problems are enormous challenges in practical recommender systems. One promising solution for this problem is cross-domain recommendation (CDR) which leverages rich information from an auxiliary (source) domain to improve the performance of recommender system …

Towards Robust Knowledge Graph Embedding via Multi-Task Reinforcement Learning

2021 · IEEE Transactions on Knowledge and Data Engineering

Nowadays, Knowledge graphs (KGs) have been playing a pivotal role in AI-related applications. Despite the large sizes, existing KGs are far from complete and comprehensive. In order to continuously enrich KGs, automatic knowledge construction and …

TAR: Neural Logical Reasoning across TBox and ABox

2022 · arXiv (Cornell University)

Many ontologies, i.e., Description Logic (DL) knowledge bases, have been developed to provide rich knowledge about various domains. An ontology consists of an ABox, i.e., assertion axioms between two entities or between a concept and …

Along the Time

2022 · Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Recent years have witnessed remarkable progress on knowledge graph embedding (KGE) methods to learn the representations of entities and relations in static knowledge graphs (SKGs). However, knowledge changes over time. In order to represent the …

Selective Fairness in Recommendation via Prompts

2022 · arXiv (Cornell University)

Recommendation fairness has attracted great attention recently. In real-world systems, users usually have multiple sensitive attributes (e.g. age, gender, and occupation), and users may not want their recommendation results influenced by those attributes. Moreover, which …

Hierarchical Neural Topic Model with Embedding Cluster and Neural Variational Inference

2023 · Society for Industrial and Applied Mathematics eBooks

Compared to flat topic models, hierarchical topic models not only exploit inherent structural information in the corpus but detect better semantic topics with the help of hierarchy knowledge. Recently, Neural-Variational-Inference (NVI) based hierarchical neural topic …

Seq-HGNN: Learning Sequential Node Representation on Heterogeneous Graph

2023

Recent years have witnessed the rapid development of heterogeneous graph neural networks (HGNNs) in information retrieval (IR) applications. Many existing HGNNs design a variety of tailor-made graph convolutions to capture structural and semantic information in …

Attacking Pre-trained Recommendation

2023

Recently, a series of pioneer studies have shown the potency of pre-trained models in sequential recommendation, illuminating the path of building an omniscient unified pre-trained recommendation model for different downstream recommendation tasks. Despite these advancements, …

A Survey on Knowledge Graph-Based Recommender Systems : Extended Abstract

2023

To solve the information explosion problem and enhance user experience in various online applications, recommender systems have been developed to model users’ preferences. Although numerous efforts have been made toward more personalized recommendations, recommender systems …

Multi-round Counterfactual Generation: Interpreting and Improving Models of Text Classification

2024

In recent years, natural language processing (NLP) models have demonstrated remarkable performance in text classification tasks. However, trust in the decision-making process requires a deeper understanding of the operational principles of these networks. Therefore, there …

Attention-driven Factor Model for Explainable Personalized Recommendation

2018

Latent Factor Models (LFMs) based on Collaborative Filtering (CF) have been widely applied in many recommendation systems, due to their good performance of prediction accuracy. In addition to users' ratings, auxiliary information such as item …

Sequential Recommender System based on Hierarchical Attention Networks

2018

With a large amount of user activity data accumulated, it is crucial to exploit user sequential behavior for sequential recommendations. Conventionally, user general taste and recent demand are combined to promote recommendation performances. However, existing …

Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation

2019 · Proceedings of the AAAI Conference on Artificial Intelligence

Next Point-of-Interest (POI) recommendation is of great value for both location-based service providers and users. However, the state-of-the-art Recurrent Neural Networks (RNNs) rarely consider the spatio-temporal intervals between neighbor check-ins, which are essential for modeling …

Graph Contextualized Self-Attention Network for Session-based Recommendation

2019

Session-based recommendation, which aims to predict the user's immediate next action based on anonymous sessions, is a key task in many online services (e.g., e-commerce, media streaming). Recently, Self-Attention Network (SAN) has achieved significant success …

PromptBERT: Improving BERT Sentence Embeddings with Prompts

2022

Ting Jiang, Jian Jiao, Shaohan Huang, Zihan Zhang, Deqing Wang, Fuzhen Zhuang, Furu Wei, Haizhen Huang, Denvy Deng, Qi Zhang. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 2022.