Tat‐Seng Chua

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, …

Generative Topic Embedding: a Continuous Representation of Documents

2016

Word embedding maps words into a lowdimensional continuous embedding space by exploiting the local word collocation patterns in a small context window. On the other hand, topic modeling maps documents onto a low-dimensional topic space, …

Recommendation Technologies for Multimedia Content

2018

Recommendation systems play a vital role in online information systems and have become a major monetization tool for user-oriented platforms. In recent years, there has been increasing research interest in recommendation technologies in the information …

Contrastive Learning for Cold-Start Recommendation

2021 · arXiv (Cornell University)

Recommending cold-start items is a long-standing and fundamental challenge in recommender systems. Without any historical interaction on cold-start items, CF scheme fails to use collaborative signals to infer user preference on these items. To solve …

How Knowledge Graph and Attention Help? A Quantitative Analysis into Bag-level Relation Extraction

2021 · arXiv (Cornell University)

Knowledge Graph (KG) and attention mechanism have been demonstrated effective in introducing and selecting useful information for weakly supervised methods. However, only qualitative analysis and ablation study are provided as evidence. In this paper, we …

Leveraging two types of global graph for sequential fashion recommendation

2021 · Singapore Management University Institutional Knowledge (InK) (Singapore Management University)

Sequential fashion recommendation is of great significance in online fashion shopping, which accounts for an increasing portion of either fashion retailing or online e-commerce. The key to building an effective sequential fashion recommendation model lies …

Causal Disentangled Recommendation against User Preference Shifts

2023 · ACM Transactions on Information Systems

Recommender systems easily face the issue of user preference shifts. User representations will become out-of-date and lead to inappropriate recommendations if user preference has shifted over time. To solve the issue, existing work focuses on …

Improving Named Entity Recognition via Bridge-based Domain Adaptation

2023

Recent studies have shown remarkable success in cross-domain named entity recognition (cross-domain NER). Despite the promising results, existing methods mainly utilize pre-training language models like BERT to represent words. As such, the original chaotic representations …

MultiCBR: Multi-view Contrastive Learning for Bundle Recommendation

2023 · arXiv (Cornell University)

Bundle recommendation seeks to recommend a bundle of related items to users to improve both user experience and the profits of platform. Existing bundle recommendation models have progressed from capturing only user-bundle interactions to the …

Data-efficient Fine-tuning for LLM-based Recommendation

2024 · arXiv (Cornell University)

Leveraging Large Language Models (LLMs) for recommendation has recently garnered considerable attention, where fine-tuning plays a key role in LLMs' adaptation. However, the cost of fine-tuning LLMs on rapidly expanding recommendation data limits their practical …

Analyzing Temporal Complex Events with Large Language Models? A Benchmark towards Temporal, Long Context Understanding

2024 · arXiv (Cornell University)

The digital landscape is rapidly evolving with an ever-increasing volume of online news, emphasizing the need for swift and precise analysis of complex events. We refer to the complex events composed of many news articles …

Efficient Inference for Large Language Model-based Generative Recommendation

2024 · arXiv (Cornell University)

Large Language Model (LLM)-based generative recommendation has achieved notable success, yet its practical deployment is costly particularly due to excessive inference latency caused by autoregressive decoding. For lossless LLM decoding acceleration, Speculative Decoding (SD) has …

Causality-Enhanced Behavior Sequence Modeling in LLMs for Personalized Recommendation

2024 · arXiv (Cornell University)

Recent advancements in recommender systems have focused on leveraging Large Language Models (LLMs) to improve user preference modeling, yielding promising outcomes. However, current LLM-based approaches struggle to fully leverage user behavior sequences, resulting in suboptimal …

Attend and Enrich: Enhanced Visual Prompt for Zero-Shot Learning

2025 · Proceedings of the AAAI Conference on Artificial Intelligence

Zero-shot learning (ZSL) endeavors to transfer knowledge from the seen categories to recognize unseen categories, which mostly relies on the semantic-visual interactions between image and attribute tokens. Recently, the prompt learning has emerged in ZSL …

Personalized Text Generation with Contrastive Activation Steering

2025

Jinghao Zhang, Yuting Liu, Wenjie Wang, Qiang Liu, Shu Wu, Liang Wang, Tat-Seng Chua. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Learning Image and User Features for Recommendation in Social Networks

2015

Good representations of data do help in many machine learning tasks such as recommendation. It is often a great challenge for traditional recommender systems to learn representative features of both users and images in large …

Topical Word Embeddings

2015 · Proceedings of the AAAI Conference on Artificial Intelligence

Most word embedding models typically represent each word using a single vector, which makes these models indiscriminative for ubiquitous homonymy and polysemy. In order to enhance discriminativeness, we employ latent topic models to assign topics …

Discrete Collaborative Filtering

2016

We address the efficiency problem of Collaborative Filtering (CF) by hashing users and items as latent vectors in the form of binary codes, so that user-item affinity can be efficiently calculated in a Hamming space. …

Fast Matrix Factorization for Online Recommendation with Implicit Feedback

2016

This paper contributes improvements on both the effectiveness and efficiency of Matrix Factorization (MF) methods for implicit feedback. We highlight two critical issues of existing works. First, due to the large space of unobserved feedback, …

Neural Collaborative Filtering

2017 · arXiv (Cornell University)

In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks on recommender systems has received relatively less scrutiny. In …

Neural Factorization Machines for Sparse Predictive Analytics

2017 · arXiv (Cornell University)

Many predictive tasks of web applications need to model categorical variables, such as user IDs and demographics like genders and occupations. To apply standard machine learning techniques, these categorical predictors are always converted to a …

Attentive Collaborative Filtering

2017

Multimedia content is dominating today's Web information. The nature of multimedia user-item interactions is 1/0 binary implicit feedback (e.g., photo likes, video views, song downloads, etc.), which can be collected at a larger scale with …

TEM

2018

While collaborative filtering is the dominant technique in personalized recommendation, it models user-item interactions only and cannot provide concrete reasons for a recommendation. Meanwhile, the rich side information affiliated with user-item interactions (e.g., user demographics …

Adversarial Personalized Ranking for Recommendation

2018

Item recommendation is a personalized ranking task. To this end, many recommender systems optimize models with pairwise ranking objectives, such as the Bayesian Personalized Ranking (BPR). Using matrix Factorization (MF) - the most widely used …

NAIS: Neural Attentive Item Similarity Model for Recommendation

2018 · IEEE Transactions on Knowledge and Data Engineering

Item-to-item collaborative filtering (aka.item-based CF) has been long used for building recommender systems in industrial settings, owing to its interpretability and efficiency in real-time personalization. It builds a user's profile as her historically interacted items, …

Explainable Reasoning over Knowledge Graphs for Recommendation

2018 · arXiv (Cornell University)

Incorporating knowledge graph into recommender systems has attracted increasing attention in recent years. By exploring the interlinks within a knowledge graph, the connectivity between users and items can be discovered as paths, which provide rich …

Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences

2019

Incorporating knowledge graph (KG) into recommender system is promising in improving the recommendation accuracy and explainability. However, existing methods largely assume that a KG is complete and simply transfer the ”knowledge” in KG at the …

KGAT

2019

To provide more accurate, diverse, and explainable recommendation, it is compulsory to go beyond modeling user-item interactions and take side information into account. Traditional methods like factorization machine (FM) cast it as a supervised learning …

Attributed Social Network Embedding

2018 · IEEE Transactions on Knowledge and Data Engineering

Embedding network data into a low-dimensional vector space has shown promising performance for many real-world applications, such as node classification and entity retrieval. However, most existing methods focused only on leveraging network structure. For social …

Outer Product-based Neural Collaborative Filtering

2018

In this work, we contribute a new multi-layer neural network architecture named ONCF to perform collaborative filtering. The idea is to use an outer product to explicitly model the pairwise correlations between the dimensions of …

Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks

2017

Factorization Machines (FMs) are a supervised learning approach that enhances the linear regression model by incorporating the second-order feature interactions. Despite effectiveness, FM can be hindered by its modelling of all feature interactions with the …

Graph Neural Networks with Generated Parameters for Relation Extraction

2019

10.18653/v1/P19-1128

Low-Resource Name Tagging Learned with Weakly Labeled Data

2019

Yixin Cao, Zikun Hu, Tat-seng Chua, Zhiyuan Liu, Heng Ji. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

MMGCN

2019

Personalized recommendation plays a central role in many online content sharing platforms. To provide quality micro-video recommendation service, it is of crucial importance to consider the interactions between users and items (i.e. micro-videos) as well …

Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems

2020

Recommender systems are embracing conversational technologies to obtain user preferences dynamically, and to overcome inherent limitations of their static models. A successful Conversational Recommender System (CRS) requires proper handling of interactions between conversation and recommendation. …

Expertise Style Transfer: A New Task Towards Better Communication between Experts and Laymen

2020

The curse of knowledge can impede communication between experts and laymen. We propose a new task of expertise style transfer and contribute a manually annotated dataset with the goal of alleviating such cognitive biases. Solving …

Reinforced negative sampling over knowledge graph for recommendation

2020 · Singapore Management University Institutional Knowledge (InK) (Singapore Management University)

National Research Foundation (NRF) Singapore under International Research Centre in Singapore Funding Initiative

Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering

2021 · arXiv (Cornell University)

Open-domain Question Answering (OpenQA) is an important task in Natural Language Processing (NLP), which aims to answer a question in the form of natural language based on large-scale unstructured documents. Recently, there has been a …

Learning Intents behind Interactions with Knowledge Graph for Recommendation

2021

Knowledge graph (KG) plays an increasingly important role in recommender systems. A recent technical trend is to develop end-to-end models founded on graph neural networks (GNNs). However, existing GNN-based models are coarse-grained in relational modeling, …

A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models

2024

As one of the most advanced techniques in AI, Retrieval-Augmented Generation (RAG) can offer reliable and up-to-date external knowledge, providing huge convenience for numerous tasks. Particularly in the era of AI-Generated Content (AIGC), the powerful …