Weinan Zhang

Annotating Needles in the Haystack without Looking

2015

Business-to-consumer (B2C) emails are usually generated by filling structured user data (e.g.purchase, event) into templates. Extracting structured data from B2C emails allows users to track important information on various devices.

Capturing the Semantics of Key Phrases Using Multiple Languages for Question Retrieval

2015 · IEEE Transactions on Knowledge and Data Engineering

In the age of Web 2.0, community user contributed questions and answers provide an important alternative for knowledge acquisition through web search. Question retrieval in current community-based question answering (CQA) services do not, in general, …

Discriminative Sentence Modeling for Story Ending Prediction

2020 · Proceedings of the AAAI Conference on Artificial Intelligence

Story Ending Prediction is a task that needs to select an appropriate ending for the given story, which requires the machine to understand the story and sometimes needs commonsense knowledge. To tackle this task, we …

DropNAS: Grouped Operation Dropout for Differentiable Architecture Search

2022 · arXiv (Cornell University)

Neural architecture search (NAS) has shown encouraging results in automating the architecture design. Recently, DARTS relaxes the search process with a differentiable formulation that leverages weight-sharing and SGD where all candidate operations are trained simultaneously. …

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 …

An F-shape Click Model for Information Retrieval on Multi-block Mobile Pages

2022 · arXiv (Cornell University)

To provide click simulation or relevance estimation based on users' implicit interaction feedback, click models have been much studied during recent years. Most click models focus on user behaviors towards a single list. However, with …

GraphHINGE: Learning Interaction Models of Structured Neighborhood on Heterogeneous Information Network

2020 · arXiv (Cornell University)

Heterogeneous information network (HIN) has been widely used to characterize entities of various types and their complex relations. Recent attempts either rely on explicit path reachability to leverage path-based semantic relatedness or graph neighborhood to …

A Bird's-eye View of Reranking: from List Level to Page Level

2022 · arXiv (Cornell University)

Reranking, as the final stage of multi-stage recommender systems, refines the initial lists to maximize the total utility. With the development of multimedia and user interface design, the recommendation page has evolved to a multi-list …

Set-to-Sequence Ranking-based Concept-aware Learning Path Recommendation

2023 · arXiv (Cornell University)

With the development of the online education system, personalized education recommendation has played an essential role. In this paper, we focus on developing path recommendation systems that aim to generating and recommending an entire learning …

Is Risk-Sensitive Reinforcement Learning Properly Resolved?

2023 · arXiv (Cornell University)

Due to the nature of risk management in learning applicable policies, risk-sensitive reinforcement learning (RSRL) has been realized as an important direction. RSRL is usually achieved by learning risk-sensitive objectives characterized by various risk measures, …

On Realization of Intelligent Decision Making in the Real World: A Foundation Decision Model Perspective

2023 · CAAI Artificial Intelligence Research

The pervasive uncertainty and dynamic nature of real-world environments present significant challenges for the widespread implementation of machine-driven Intelligent Decision-Making (IDM) systems. Consequently, IDM should possess the ability to continuously acquire new skills and effectively …

DREAM: A Dual Representation Learning Model for Multimodal Recommendation

2024 · arXiv (Cornell University)

Multimodal recommendation focuses primarily on effectively exploiting both behavioral and multimodal information for the recommendation task. However, most existing models suffer from the following issues when fusing information from two different domains: (1) Previous works …

Fast Second-Order Online Kernel Learning Through Incremental Matrix Sketching and Decomposition

2024

Second-order Online Kernel Learning (OKL) has attracted considerable research interest due to its promising predictive performance in streaming environments. However, existing second-order OKL approaches suffer from at least quadratic time complexity with respect to the …

OpenR: An Open Source Framework for Advanced Reasoning with Large Language Models

2024 · arXiv (Cornell University)

In this technical report, we introduce OpenR, an open-source framework designed to integrate key components for enhancing the reasoning capabilities of large language models (LLMs). OpenR unifies data acquisition, reinforcement learning training (both online and …

Unleashing the Potential of Multi-Channel Fusion in Retrieval for Personalized Recommendations

2024 · arXiv (Cornell University)

Recommender systems (RS) are pivotal in managing information overload in modern digital services. A key challenge in RS is efficiently processing vast item pools to deliver highly personalized recommendations under strict latency constraints. Multi-stage cascade …

Product-Based Neural Networks for User Response Prediction

2016

Predicting user responses, such as clicks and conversions, is of great importance and has found its usage inmany Web applications including recommender systems, websearch and online advertising. The data in those applicationsis mostly categorical and …

Texygen: A Benchmarking Platform for Text Generation Models

2018 · arXiv (Cornell University)

We introduce Texygen, a benchmarking platform to support research on open-domain text generation models. Texygen has not only implemented a majority of text generation models, but also covered a set of metrics that evaluate the …

Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction

2019

User response prediction, which models the user preference w.r.t. the presented items, plays a key role in online services. With two-decade rapid development, nowadays the cumulated user behavior sequences on mature Internet service platforms have …

Dynamically Fused Graph Network for Multi-hop Reasoning

2019

Text-based question answering (TBQA) has been studied extensively in recent years. Most existing approaches focus on finding the answer to a question within a single paragraph. However, many difficult questions require multiple supporting evidence from …

Label-Aware Double Transfer Learning for Cross-Specialty Medical Named Entity Recognition

2018

Zhenghui Wang, Yanru Qu, Liheng Chen, Jian Shen, Weinan Zhang, Shaodian Zhang, Yimei Gao, Gen Gu, Ken Chen, Yong Yu. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational …

Long Text Generation via Adversarial Training with Leaked Information

2018 · Proceedings of the AAAI Conference on Artificial Intelligence

Automatically generating coherent and semantically meaningful text has many applications in machine translation, dialogue systems, image captioning, etc. Recently, by combining with policy gradient, Generative Adversarial Nets(GAN) that use a discriminative model to guide the …

Texygen

2018

We introduce Texygen, a benchmarking platform to support research on open-domain text generation models. Texygen has not only implemented a majority of text generation models, but also covered a set of metrics that evaluate the …

Large-Scale Interactive Recommendation with Tree-Structured Policy Gradient

2019 · Proceedings of the AAAI Conference on Artificial Intelligence

Reinforcement learning (RL) has recently been introduced to interactive recommender systems (IRS) because of its nature of learning from dynamic interactions and planning for long-run performance. As IRS is always with thousands of items to …

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 …

User Behavior Retrieval for Click-Through Rate Prediction

2020

Click-through rate (CTR) prediction plays a key role in modern online personalization services. In practice, it is necessary to capture user's drifting interests by modeling sequential user behaviors to build an accurate CTR prediction model. …

An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph

2020

There is an influx of heterogeneous information network (HIN) based recommender systems in recent years since HIN is capable of characterizing complex graphs and contains rich semantics. Although the existing approaches have achieved performance improvement, …

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 …