Xiuqiang He
13 papers in the PaperMetrix corpus
Papers by this author
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DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
2017 · arXiv (Cornell University)
Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems. Despite great progress, existing methods seem to have a strong bias towards low- or high-order interactions, or require expertise feature …
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JIT2R: A Joint Framework for Item Tagging and Tag-based Recommendation
2020
Predicting tags for a given item and leveraging tags to assist item recommendation are two popular research topics in the field of recommender system. Previous studies mostly focus only one of them to make contributions. …
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A Framework for Recommending Accurate and Diverse Items Using Bayesian Graph Convolutional Neural Networks
2020
Personalized recommender systems are playing an increasingly important role for online consumption platforms. Because of the multitude of relationships existing in recommender systems, Graph Neural Networks (GNNs) based approaches have been proposed to better characterize …
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RMBERT: News Recommendation via Recurrent Reasoning Memory Network over BERT
2021
Personalized news recommendation aims to alleviate information overload and help users find news of their interests. Accurately matching candidate news and users' interests is the key to news recommendation. Most existing methods separately encode each …
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AutoFT: Automatic Fine-Tune for Parameters Transfer Learning in Click-Through Rate Prediction
2021 · arXiv (Cornell University)
Recommender systems are often asked to serve multiple recommendation scenarios or domains. Fine-tuning a pre-trained CTR model from source domains and adapting it to a target domain allows knowledge transferring. However, optimizing all the parameters …
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Dual Sequence Transformer for Query-based Interactive Recommendation
2021
Interactive recommendation has drawn widespread attention from both academia and industry due to its effectiveness in real-world mobile applications. Instead of receiving message passively, customers can exploit further with less effort through generated queries. Usually, …
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UNBERT: User-News Matching BERT for News Recommendation
2021
Nowadays, news recommendation has become a popular channel for users to access news of their interests. How to represent rich textual contents of news and precisely match users' interests and candidate news lies in the …
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Retrieval & Interaction Machine for Tabular Data Prediction
2021 · arXiv (Cornell University)
Prediction over tabular data is an essential task in many data science applications such as recommender systems, online advertising, medical treatment, etc. Tabular data is structured into rows and columns, with each row as a …
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Enhancing Explicit and Implicit Feature Interactions via Information Sharing for Parallel Deep CTR Models
2021
Effectively modeling feature interactions is crucial for CTR prediction in industrial recommender systems. The state-of-the-art deep CTR models with parallel structure (e.g., DCN) learn explicit and implicit feature interactions through independent parallel networks. However, these …
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UltraGCN
2021
With the recent success of graph convolutional networks (GCNs), they have been widely applied for recommendation, and achieved impressive performance gains. The core of GCNs lies in its message passing mechanism to aggregate neighborhood information. …
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Product-Based Neural Networks for User Response Prediction over Multi-Field Categorical Data
2018 · ACM Transactions on Information Systems
User response prediction is a crucial component for personalized information retrieval and filtering scenarios, such as recommender system and web search. The data in user response prediction is mostly in a multi-field categorical format and …
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AutoFIS
2020
Learning feature interactions is crucial for click-through rate (CTR) prediction in recommender systems. In most existing deep learning models, feature interactions are either manually designed or simply enumerated. However, enumerating all feature interactions brings large …
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SimpleX
2021
Collaborative filtering (CF) is a widely studied research topic in recommender systems. The learning of a CF model generally depends on three major components, namely interaction encoder, loss function, and negative sampling. While many existing …