Xu Chen

Predicting the Mumble of Wireless Channel with Sequence-to-Sequence Models

2019

Accurate prediction of fading channel in the upcoming transmission frame is essential to realize adaptive transmission for transmitters, and receivers with the ability of channel prediction can also save some computations of channel estimation. However, …

XBlock-EOS: Extracting and Exploring Blockchain Data From EOSIO

2020 · arXiv (Cornell University)

Blockchain-based cryptocurrencies and applications have flourished in blockchain research community. Massive data generated from diverse blockchain systems bring not only huge business values but also technological challenges in data analytics of heterogeneous blockchain data. Different …

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. …

Edgeconomics: Price Competition and Selfish Computation Offloading in Multi-Server Edge Computing Networks

2021

As edge computing provides crucial support for delay-sensitive and computation-intensive applications, many business entities deploy their own edge servers to compete for users, which forms multi-server edge computing networks. However, no prior work studies the …

Unbiased Sequential Recommendation with Latent Confounders

2022 · Proceedings of the ACM Web Conference 2022

Sequential recommendation holds the promise of understanding user preference by capturing successive behavior correlations. Existing research focus on designing different models for better fitting the offline datasets. However, the observational data may have been contaminated …

Sequential Recommendation with Decomposed Item Feature Routing

2022 · Proceedings of the ACM Web Conference 2022

Sequential recommendation basically aims to capture user evolving preference. Intuitively, a user interacts with an item usually because of some specific feature, and user evolving preference is essentially determined by a series of important features …

Distributionally Robust Sequential Recommnedation

2023

Modeling user sequential behaviors have been demonstrated to be effective in promoting the recommendation performance. While previous work has achieved remarkable successes, they mostly assume that the training and testing distributions are consistent, which may …

Chained-DP: Can We Recycle Privacy Budget?

2023

Privacy-preserving vector mean estimation is a crucial primitive in federated analytics. Existing practices usually resort to Local Differentiated Privacy (LDP) mechanisms that inject random noise into users' vectors when communicating with users and the central …

DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Dataset

2023 · arXiv (Cornell University)

Federated Learning (FL) is a distributed learning paradigm that can coordinate heterogeneous edge devices to perform model training without sharing private data. While prior works have focused on analyzing FL convergence with respect to hyperparameters …

Wehrl Entropy and Entanglement Complexity of Quantum Spin Systems

2023 · arXiv (Cornell University)

The Wehrl entropy of a quantum state is the Shannon entropy of its coherent-state distribution function, and remains non-zero even for pure states. We investigate the relationship between this entropy and the many-particle quantum entanglement, …

TAROT: A Hierarchical Framework with Multitask Co-Pretraining on Semi-Structured Data towards Effective Person-Job Fit

2024 · arXiv (Cornell University)

Person-job fit is an essential part of online recruitment platforms in serving various downstream applications like Job Search and Candidate Recommendation. Recently, pretrained large language models have further enhanced the effectiveness by leveraging richer textual …

YuLan: An Open-source Large Language Model

2024 · arXiv (Cornell University)

Large language models (LLMs) have become the foundation of many applications, leveraging their extensive capabilities in processing and understanding natural language. While many open-source LLMs have been released with technical reports, the lack of training …

Adaptive Privacy Budget Allocation in Federated Learning: A Multi-Agent Reinforcement Learning Approach

2024

Federated learning is a popular distributed machine learning paradigm that keeps data locally at clients. To further enhance privacy protection, differential privacy techniques are incorporated in the federated learning framework. We can quantify the privacy …

Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence

2024 · arXiv (Cornell University)

Recent years have witnessed a thriving growth of computing facilities connected at the network edge, cultivating edge networks as a fundamental infrastructure for supporting miscellaneous intelligent services.Meanwhile, Artificial Intelligence (AI) frontiers have extrapolated to the …

Microns: Connection Subsetting for Microservices in Shared Clusters

2025

Microservice applications typically employ a technique known as connection subsetting to ensure resource-efficient and stable communication with persistent connections. However, the interdependency in microservice applications and complex runtime environments pose significant challenges for effective connection …

Learning to Rank Features for Recommendation over Multiple Categories

2016

Incorporating phrase-level sentiment analysis on users' textual reviews for recommendation has became a popular meth-od due to its explainable property for latent features and high prediction accuracy. However, the inherent limitations of the existing model …

Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources

2017

The Web has accumulated a rich source of information, such as text, image, rating, etc, which represent different aspects of user preferences. However, the heterogeneous nature of this information makes it difficult for recommender systems …

Sequential Recommendation with User Memory Networks

2018

User preferences are usually dynamic in real-world recommender systems, and a user»s historical behavior records may not be equally important when predicting his/her future interests. Existing recommendation algorithms -- including both shallow and deep approaches …

Explainable Recommendation: A Survey and New Perspectives

2020 · Foundations and Trends® in Information Retrieval

Explainable recommendation attempts to develop models that generate not only high-quality recommendations but also intuitive explanations. The explanations may either be post-hoc or directly come from an explainable model (also called interpretable or transparent model …

Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation

2018 · Algorithms

Providing model-generated explanations in recommender systems is important to user experience. State-of-the-art recommendation algorithms—especially the collaborative filtering (CF)- based approaches with shallow or deep models—usually work with various unstructured information sources for recommendation, such as …

Towards Conversational Search and Recommendation

2018

Conversational search and recommendation based on user-system dialogs exhibit major differences from conventional search and recommendation tasks in that 1) the user and system can interact for multiple semantically coherent rounds on a task through …

CAFE

2020

Recent research explores incorporating knowledge graphs (KG) into e-commerce recommender systems, not only to achieve better recommendation performance, but more importantly to generate explanations of why particular decisions are made. This can be achieved by …

Counterfactual Data-Augmented Sequential Recommendation

2021

Sequential recommendation aims at predicting users' preferences based on their historical behaviors. However, this recommendation strategy may not perform well in practice due to the sparsity of the real-world data. In this paper, we propose …

RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms

2021

In recent years, there are a large number of recommendation algorithms proposed in the literature, from traditional collaborative filtering to deep learning algorithms. However, the concerns about how to standardize open source implementation of recommendation …