Enhong Chen

MARS: A multi-aspect Recommender system for Point-of-Interest

2015

With the pervasive use of GPS-enabled smart phones, location-based services, e.g., Location Based Social Networking (LBSN) have emerged . Point-of-Interests (POIs) Recommendation, as a typical component in LBSN, provides additional values to both customers and …

Exploiting Task-Feature Co-Clusters in Multi-Task Learning

2015 · Proceedings of the AAAI Conference on Artificial Intelligence

In multi-task learning, multiple related tasks are considered simultaneously, with the goal to improve the generalization performance by utilizing the intrinsic sharing of information across tasks. This paper presents a multi-task learning approach by modeling …

Confidence-Aware Matrix Factorization for Recommender Systems

2018 · Proceedings of the AAAI Conference on Artificial Intelligence

Collaborative filtering (CF), particularly matrix factorization (MF) based methods, have been widely used in recommender systems. The literature has reported that matrix factorization methods often produce superior accuracy of rating prediction in recommender systems. However, …

Interactive Attention Transfer Network for Cross-Domain Sentiment Classification

2019 · Proceedings of the AAAI Conference on Artificial Intelligence

Cross-domain sentiment classification refers to utilizing useful knowledge in the source domain to help sentiment classification in the target domain which has few or no labeled data. Most existing methods mainly concentrate on extracting common …

DRr-Net: Dynamic Re-Read Network for Sentence Semantic Matching

2019 · Proceedings of the AAAI Conference on Artificial Intelligence

Sentence semantic matching requires an agent to determine the semantic relation between two sentences, which is widely used in various natural language tasks such as Natural Language Inference (NLI) and Paraphrase Identification (PI). Among all …

Learning Better Word Embedding by Asymmetric Low-Rank Projection of Knowledge Graph

2015 · arXiv (Cornell University)

Word embedding, which refers to low-dimensional dense vector representations of natural words, has demonstrated its power in many natural language processing tasks. However, it may suffer from the inaccurate and incomplete information contained in the …

Semi-Supervised Neural Machine Translation via Marginal Distribution Estimation

2019 · IEEE/ACM Transactions on Audio Speech and Language Processing

Neural machine translation (NMT) heavily relies on parallel bilingual corpora for training. Since large-scale, high-quality parallel corpora are usually costly to collect, it is appealing to exploit monolingual corpora to improve NMT. Inspired by the …

Alpha-Beta Sampling for Pairwise Ranking in One-Class Collaborative Filtering

2019

This paper introduces Alpha-Beta Sampling (ABS) strategy, which is particularly intended for the sampling problem of pairwise ranking in one-class collaborative filtering (PROCCF). Specifically, ABS strategy places more emphasis on such training examples, including positive …

Ontological Concept Structure Aware Knowledge Transfer for Inductive Knowledge Graph Embedding

2021

Conventional knowledge graph embedding methods mainly assume that all entities at reasoning stage are available in the original training graph. But in real-world application scenarios, newly emerged entities are always inevitable, which results in the …

Towards Automatic Discovering of Deep Hybrid Network Architecture for Sequential Recommendation

2022 · Proceedings of the ACM Web Conference 2022

Recent years have witnessed great success in deep learning-based sequential recommendation (SR), which can provide more timely and accurate recommendations. One of the most effective deep SR architectures is to stack high-performance residual blocks, e.g., …

CBR: Context Bias aware Recommendation for Debiasing User Modeling and Click Prediction

2022 · Proceedings of the ACM Web Conference 2022

With the prosperity of recommender systems, the biases existing in user behaviors, which may lead to inconsistency between user preference and behavior records, have attracted wide attention. Though large efforts have been made to infer …

A Novel Approach for Auto-Formulation of Optimization Problems

2023 · arXiv (Cornell University)

In the Natural Language for Optimization (NL4Opt) NeurIPS 2022 competition, competitors focus on improving the accessibility and usability of optimization solvers, with the aim of subtask 1: recognizing the semantic entities that correspond to the …

Promoting Machine Abilities of Discovering and Utilizing Knowledge in a Unified Zero-Shot Learning Paradigm

2024 · ACM Transactions on Knowledge Discovery from Data

Knowledge discovery and utilization are two essential cognitive processes that enable humans to understand the world and extract new insights from their surroundings. These processes have motivated machine learning studies, particularly zero-shot (ZS) learning, which …

A Multi-scale Feature Learning Network with Optical Flow Correction for Micro- and Macro-expression Spotting

2024

Recently, automatic micro-expression (ME) analysis has attracted increasing attention, since ME is a spontaneous facial expression that can truly reflect the emotional state an individual tries to conceal. As a crucial step in ME analysis, …

MIT: A Multi-Tower Information Transfer Framework Based on Hierarchical Task Relationship Modeling

2025

With the advancement of e-commerce platforms and online recommendation systems, conversion objectives have evolved from singular to diverse goals. To simultaneously enhance the performance of multiple conversion objectives, multi-task learning has become a classic solution. …

Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis

2025

In the quest for artificial general intelligence, Multi-modal Large Language Models (MLLMs) have emerged as a focal point in recent advancements. However, the predominant focus remains on developing their capabilities in static image understanding. The …

SimCDR: Preserving Intra-Domain Similarities of Users for Cross-Domain Recommendation

2025 · ACM Transactions on Information Systems

Cross-Domain Recommendation (CDR) can effectively alleviate the data sparsity issue in the recommendation system by transferring the source domain knowledge to the target domain. Many CDR methods try to find a mapping of latent embeddings …

Learning from History and Present

2018

In the modern e-commerce, the behaviors of customers contain rich information, e.g., consumption habits, the dynamics of preferences. Recently, session-based recommendationsare becoming popular to explore the temporal characteristics of customers' interactive behaviors. However, existing works …

Style Transfer as Unsupervised Machine Translation

2018 · arXiv (Cornell University)

Language style transferring rephrases text with specific stylistic attributes while preserving the original attribute-independent content. One main challenge in learning a style transfer system is a lack of parallel data where the source sentence is …

Co-Attentive Multi-Task Learning for Explainable Recommendation

2019

Despite widespread adoption, recommender systems remain mostly black boxes. Recently, providing explanations about why items are recommended has attracted increasing attention due to its capability to enhance user trust and satisfaction. In this paper, we …

Geography-Aware Sequential Location Recommendation

2020

Sequential location recommendation plays an important role in many applications such as mobility prediction, route planning and location-based advertisements. In spite of evolving from tensor factorization to RNN-based neural networks, existing methods did not make …

Multi-Interactive Attention Network for Fine-grained Feature Learning in CTR Prediction

2021

In the Click-Through Rate (CTR) prediction scenario, user's sequential behaviors are well utilized to capture the user interest in the recent literature. However, despite being extensively studied, these sequential methods still suffer from three limitations. …

Large language models for generative information extraction: a survey

2024 · Frontiers of Computer Science

Abstract Information Extraction (IE) aims to extract structural knowledge from plain natural language texts. Recently, generative Large Language Models (LLMs) have demonstrated remarkable capabilities in text understanding and generation. As a result, numerous works have …

A survey on multimodal large language models

2024 · National Science Review

Recently, the multimodal large language model (MLLM) represented by GPT-4V has been a new rising research hotspot, which uses powerful large language models (LLMs) as a brain to perform multimodal tasks. The surprising emergent capabilities …