Irwin King

Title-Guided Encoding for Keyphrase Generation

2018 · arXiv (Cornell University)

Keyphrase generation (KG) aims to generate a set of keyphrases given a document, which is a fundamental task in natural language processing (NLP). Most previous methods solve this problem in an extractive manner, while recently, …

Retrieval-Augmented Multilingual Keyphrase Generation with Retriever-Generator Iterative Training

2022 · Findings of the Association for Computational Linguistics: NAACL 2022

Keyphrase generation is the task of automatically predicting keyphrases given a piece of long text. Despite its recent flourishing, keyphrase generation on non-English languages haven't been vastly investigated. In this paper, we call attention to …

HICF: Hyperbolic Informative Collaborative Filtering

2022 · Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Considering the prevalence of the power-law distribution in user-item networks, hyperbolic space has attracted considerable attention and achieved impressive performance in the recommender system recently. The advantage of hyperbolic recommendation lies in that its exponentially …

Learning Binarized Graph Representations with Multi-faceted Quantization Reinforcement for Top-K Recommendation

2022 · arXiv (Cornell University)

Learning vectorized embeddings is at the core of various recommender systems for user-item matching. To perform efficient online inference, representation quantization, aiming to embed the latent features by a compact sequence of discrete numbers, recently …

Think Rationally about What You See: Continuous Rationale Extraction for Relation Extraction

2023

Relation extraction (RE) aims to extract potential relations according to the context of two entities, thus, deriving rational contexts from sentences plays an important role. Previous works either focus on how to leverage the entity …

Shopping Trajectory Representation Learning with Pre-training for E-commerce Customer Understanding and Recommendation

2024

Understanding customer behavior is crucial for improving service quality in large-scale E-commerce. This paper proposes C-STAR, a new framework that learns compact representations from customer shopping journeys, with good versatility to fuel multiple downstream customer-centric …

Fused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks

2021 · Proceedings of the AAAI Conference on Artificial Intelligence

Recently, location-based social networks (LBSNs), such as Gowalla, Foursquare, Facebook, and Brightkite, etc., have attracted millions of users to share their social friendship and their locations via check-ins. The available check-in information makes it possible …

Generating Distractors for Reading Comprehension Questions from Real Examinations

2018 · arXiv (Cornell University)

We investigate the task of distractor generation for multiple choice reading comprehension questions from examinations. In contrast to all previous works, we do not aim at preparing words or short phrases distractors, instead, we endeavor …

STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems

2019 · arXiv (Cornell University)

We propose a new STAcked and Reconstructed Graph Convolutional Networks (STAR-GCN) architecture to learn node representations for boosting the performance in recommender systems, especially in the cold start scenario. STAR-GCN employs a stack of GCN …

Difficulty Controllable Generation of Reading Comprehension Questions

2019

We investigate the difficulty levels of questions in reading comprehension datasets such as SQuAD, and propose a new question generation setting, named Difficulty-controllable Question Generation (DQG). Taking as input a sentence in the reading comprehension …

HRCF: Enhancing Collaborative Filtering via Hyperbolic Geometric Regularization

2022 · Proceedings of the ACM Web Conference 2022

In large-scale recommender systems, the user-item networks are generally scale-free or expand exponentially. For the representation of the user and item, the latent features (a.k.a, embeddings) depend on how well the embedding space matches the …