Chenliang Li

Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey

2019 · arXiv (Cornell University)

With the development of high computational devices, deep neural networks (DNNs), in recent years, have gained significant popularity in many Artificial Intelligence (AI) applications. However, previous efforts have shown that DNNs were vulnerable to strategically …

Review-Driven Answer Generation for Product-Related Questions in E-Commerce

2019 · arXiv (Cornell University)

The users often have many product-related questions before they make a purchase decision in E-commerce. However, it is often time-consuming to examine each user review to identify the desired information. In this paper, we propose …

Multi-Agent RL-based Information Selection Model for Sequential Recommendation

2022 · Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

For sequential recommender, the coarse-grained yet sparse sequential signals mined from massive user-item interactions have become the bottleneck to further improve the recommendation performance. To alleviate the spareness problem, exploiting auxiliary semantic features (\eg textual …

A Survey on Deep Learning for Named Entity Recognition : Extended Abstract

2023

Named entity recognition (NER) is the task to identify text spans that mention named entities, and to classify them into predefined categories such as person, location, organization, etc. In recent years, deep learning, empowered by …

Unified Visual Preference Learning for User Intent Understanding

2024

In the world of E-Commerce, the core task is to understand the personalized preference from various kinds of heterogeneous information, such as textual reviews, item images and historical behaviors. In current systems, these heterogeneous information …

Effective Document Labeling with Very Few Seed Words

2016

Developing text classifiers often requires a large number of labeled documents as training examples. However, manually labeling documents is costly and time-consuming. Recently, a few methods have been proposed to label documents by using a …

A Context-Aware User-Item Representation Learning for Item Recommendation

2019 · ACM Transactions on Information Systems

Both reviews and user-item interactions (i.e., rating scores) have been widely adopted for user rating prediction. However, these existing techniques mainly extract the latent representations for users and items in an independent and static manner. …

A Survey on Deep Learning for Named Entity Recognition

2020 · IEEE Transactions on Knowledge and Data Engineering

Named entity recognition (NER) is the task to identify mentions of rigid designators from text belonging to predefined semantic types such as person, location, organization etc. NER always serves as the foundation for many natural …

CATN

2020

In a large recommender system, the products (or items) could be in many different categories or domains. Given two relevant domains (e.g., Book and Movie), users may have interactions with items in one domain but …

Knowledge Graph Contrastive Learning for Recommendation

2022 · Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Knowledge Graphs (KGs) have been utilized as useful side information to improve recommendation quality. In those recommender systems, knowledge graph information often contains fruitful facts and inherent semantic relatedness among items. However, the success of …