Qing He

Formal verification for winning strategy of chess game

2017

This paper presents a formal method to verify the winning strategy in a chess game by means of symbolic model checking, and demonstrate the winning strategy in tic-tac-toe game through the symbolic model checking tool …

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 …

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 …

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 …

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 …

Efficient non-destructive direct characterization of arbitrary many-body quantum channels

2025 · npj Quantum Information

Quantum process tomography (QPT) is a crucial technique for characterizing unknown quantum channels. However, traditional QPT methods encounter scalability problems as the particle numbers increase, requiring exponentially more state preparations and measurement operators. The characteristics …

Mechanism-Aware Neural Machine for Dialogue Response Generation

2017 · Proceedings of the AAAI Conference on Artificial Intelligence

To the same utterance, people's responses in everyday dialogue may be diverse largely in terms of content semantics, speaking styles, communication intentions and so on. Previous generative conversational models ignore these 1-to-n relationships between a …

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 …