Xing Xie
46 papers in the PaperMetrix corpus
Papers by this author
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Neural Chinese Word Segmentation with Dictionary Knowledge
2018 · arXiv (Cornell University)
Chinese word segmentation (CWS) is an important task for Chinese NLP. Recently, many neural network based methods have been proposed for CWS. However, these methods require a large number of labeled sentences for model training, …
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DRr-Net: Dynamic Re-Read Network for Sentence Semantic Matching
2019 · Proceedings of the AAAI Conference on Artificial Intelligence
Sentence semantic matching requires an agent to determine the semantic relation between two sentences, which is widely used in various natural language tasks such as Natural Language Inference (NLI) and Paraphrase Identification (PI). Among all …
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Knowledge Graph Convolutional Networks for Recommender Systems
2019
To alleviate sparsity and cold start problem of collaborative filtering based recommender systems, researchers and engineers usually collect attributes of users and items, and design delicate algorithms to exploit these additional information. In general, the …
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Neural Review Rating Prediction with User and Product Memory
2019
Neural network methods have achieved great success in sentiment classification. Recent studies have found that incorporating user and product information can effectively improve the performance of review sentiment classification. However, most of these studies only …
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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 …
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Neural News Recommendation with Negative Feedback
2021 · arXiv (Cornell University)
News recommendation is important for online news services. Precise user interest modeling is critical for personalized news recommendation. Existing news recommendation methods usually rely on the implicit feedback of users like news clicks to model …
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Fairness-aware News Recommendation with Decomposed Adversarial Learning
2021
News recommendation is important for online news services. Existing news recommendation models are usually learned from users' news click behaviors. Usually the behaviors of users with the same sensitive attributes (e.g., genders) have similar patterns …
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Multi-Stage Network Embedding for Exploring Heterogeneous Edges
2021 · arXiv (Cornell University)
The relationships between objects in a network are typically diverse and complex, leading to the heterogeneous edges with different semantic information. In this paper, we focus on exploring the heterogeneous edges for network representation learning. …
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Multi-level Recommendation Reasoning over Knowledge Graphs with Reinforcement Learning
2022 · Proceedings of the ACM Web Conference 2022
Knowledge graphs (KGs) have been widely used to improve recommendation accuracy. The multi-hop paths on KGs also enable recommendation reasoning, which is considered a crystal type of explainability. In this paper, we propose a reinforcement …
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Geometric Disentangled Collaborative Filtering
2022 · Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
Learning informative representations of users and items from the historical interactions is crucial to collaborative filtering (CF). Existing CF approaches usually model interactions solely within the Euclidean space. However, the sophisticated user-item interactions inherently present …
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SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised Learning
2023 · arXiv (Cornell University)
The critical challenge of Semi-Supervised Learning (SSL) is how to effectively leverage the limited labeled data and massive unlabeled data to improve the model's generalization performance. In this paper, we first revisit the popular pseudo-labeling …
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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 …
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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 …
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Aspect-Enhanced Explainable Recommendation with Multi-modal Contrastive Learning
2024 · ACM Transactions on Intelligent Systems and Technology
Explainable recommender systems ( ERS ) aim to enhance users’ trust in the systems by offering personalized recommendations with transparent explanations. This transparency provides users with a clear understanding of the rationale behind the recommendations, …
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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 …
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Collaborative Knowledge Base Embedding for Recommender Systems
2016
Among different recommendation techniques, collaborative filtering usually suffer from limited performance due to the sparsity of user-item interactions. To address the issues, auxiliary information is usually used to boost the performance. Due to the rapid …
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DKN: Deep Knowledge-Aware Network for News Recommendation
2018 · arXiv (Cornell University)
Online news recommender systems aim to address the information explosion of news and make personalized recommendation for users. In general, news language is highly condensed, full of knowledge entities and common sense. However, existing methods …
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DRN
2018
In this paper, we propose a novel Deep Reinforcement Learning framework for news recommendation. Online personalized news recommendation is a highly challenging problem due to the dynamic nature of news features and user preferences. Although …
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RippleNet
2018
To address the sparsity and cold start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve recommendation performance. This paper considers the knowledge graph …
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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 …
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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 …
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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 …
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Session-Based Recommendation with Graph Neural Networks
2019 · Proceedings of the AAAI Conference on Artificial Intelligence
The problem of session-based recommendation aims to predict user actions based on anonymous sessions. Previous methods model a session as a sequence and estimate user representations besides item representations to make recommendations. Though achieved promising …
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Explainable Recommendation through Attentive Multi-View Learning
2019 · Proceedings of the AAAI Conference on Artificial Intelligence
Recommender systems have been playing an increasingly important role in our daily life due to the explosive growth of information. Accuracy and explainability are two core aspects when we evaluate a recommendation model and have …
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Collaborative Translational Metric Learning
2018
Recently, matrix factorization-based recommendation methods have been criticized for the problem raised by the triangle inequality violation. Although several metric learning-based approaches have been proposed to overcome this issue, existing approaches typically project each user …
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Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation
2019
Collaborative filtering often suffers from sparsity and cold start problems in real recommendation scenarios, therefore, researchers and engineers usually use side information to address the issues and improve the performance of recommender systems. In this …
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Exploring High-Order User Preference on the Knowledge Graph for Recommender Systems
2019 · ACM Transactions on Information Systems
To address the sparsity and cold-start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve the performance of recommendation. In this article, we consider …
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Co-Attentive Multi-Task Learning for Explainable Recommendation
2019
Despite widespread adoption, recommender systems remain mostly black boxes. Recently, providing explanations about why items are recommended has attracted increasing attention due to its capability to enhance user trust and satisfaction. In this paper, we …
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NPA
2019
News recommendation is very important to help users find interested news and alleviate information overload. Different users usually have different interests and the same user may have various interests. Thus, different users may click the …
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Neural News Recommendation with Long- and Short-term User Representations
2019
Personalized news recommendation is important to help users find their interested news and improve reading experience. A key problem in news recommendation is learning accurate user representations to capture their interests. Users usually have both …
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Exploring Sequence-to-Sequence Learning in Aspect Term Extraction
2019
Aspect term extraction (ATE) aims at identifying all aspect terms in a sentence and is usually modeled as a sequence labeling problem. However, sequence labeling based methods cannot make full use of the overall meaning …
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Neural News Recommendation with Topic-Aware News Representation
2019
News recommendation can help users find interested news and alleviate information overload. The topic information of news is critical for learning accurate news and user representations for news recommendation. However, it is not considered in …
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DKN
2018
Online news recommender systems aim to address the information explosion of news and make personalized recommendation for users. In general, news language is highly condensed, full of knowledge entities and common sense. However, existing methods …
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Neural News Recommendation with Attentive Multi-View Learning
2019
Personalized news recommendation is very important for online news platforms to help users find interested news and improve user experience. News and user representation learning is critical for news recommendation. Existing news recommendation methods usually …
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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 …
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Reviews Meet Graphs: Enhancing User and Item Representations for Recommendation with Hierarchical Attentive Graph Neural Network
2019
Chuhan Wu, Fangzhao Wu, Tao Qi, Suyu Ge, Yongfeng Huang, Xing Xie. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). …
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Neural News Recommendation with Multi-Head Self-Attention
2019
Chuhan Wu, Fangzhao Wu, Suyu Ge, Tao Qi, Yongfeng Huang, Xing Xie. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). …
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Leveraging Demonstrations for Reinforcement Recommendation Reasoning over Knowledge Graphs
2020
Knowledge graphs have been widely adopted to improve recommendation accuracy. The multi-hop user-item connections on knowledge graphs also endow reasoning about why an item is recommended. However, reasoning on paths is a complex combinatorial optimization …
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Fine-grained Interest Matching for Neural News Recommendation
2020
Personalized news recommendation is a critical technology to improve users' online news reading experience. The core of news recommendation is accurate matching between user's interests and candidate news. The same user usually has diverse interests …
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Graph Neural News Recommendation with Unsupervised Preference Disentanglement
2020
With the explosion of news information, personalized news recommendation has become very important for users to quickly find their interested contents. Most existing methods usually learn the representations of users and news from news contents …
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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.
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Geography-Aware Sequential Location Recommendation
2020
Sequential location recommendation plays an important role in many applications such as mobility prediction, route planning and location-based advertisements. In spite of evolving from tensor factorization to RNN-based neural networks, existing methods did not make …
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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 …
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Personalized News Recommendation: Methods and Challenges
2022 · ACM Transactions on Information Systems
Personalized news recommendation is important for users to find interesting news information and alleviate information overload. Although it has been extensively studied over decades and has achieved notable success in improving user experience, there are …
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Communication-efficient federated learning via knowledge distillation
2022 · Nature Communications
Federated learning is a privacy-preserving machine learning technique to train intelligent models from decentralized data, which enables exploiting private data by communicating local model updates in each iteration of model learning rather than the raw …
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A Survey on Evaluation of Large Language Models
2024 · ACM Transactions on Intelligent Systems and Technology
Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to play a vital role in both research and daily use, …