Xiangnan He

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 …

A Simple Convolutional Generative Network for Next Item Recommendation

2019

Convolutional Neural Networks (CNNs) have been recently introduced in the domain of session-based next item recommendation. An ordered collection of past items the user has interacted with in a session (or sequence) are embedded into …

Improving the Robustness of Wasserstein Embedding by Adversarial PAC-Bayesian Learning

2020 · Proceedings of the AAAI Conference on Artificial Intelligence

Node embedding is a crucial task in graph analysis. Recently, several methods are proposed to embed a node as a distribution rather than a vector to capture more information. Although these methods achieved noticeable improvements, …

Learning Robust Recommenders through Cross-Model Agreement

2022 · Proceedings of the ACM Web Conference 2022

Learning from implicit feedback is one of the most common cases in the application of recommender systems. Generally speaking, interacted examples are considered as positive while negative examples are sampled from uninteracted ones. However, noisy …

How to Retrain Recommender System?

2020

Practical recommender systems need be periodically retrained to refresh the model with new interaction data. To pursue high model fidelity, it is usually desirable to retrain the model on both historical and new data, since …

Popularity Bias is not Always Evil: Disentangling Benign and Harmful Bias for Recommendation

2022 · IEEE Transactions on Knowledge and Data Engineering

Recommender system usually suffers from severepopularity bias— the collected interaction data usually exhibits quite imbalanced or even long-tailed distribution over items. Such skewed distribution may result from the users’conformityto the group, which deviates from reflecting …

Interactive active learning for fairness with partial group label

2023 · AI Open

The rapid development of AI technologies has found numerous applications across various domains in human society. Ensuring fairness and preventing discrimination are critical considerations in the development of AI models. However, incomplete information often hinders …

Proactive Recommendation with Iterative Preference Guidance

2024

Recommender systems mainly tailor personalized recommendations according to user interests learned from user feedback. However, such recommender systems passively cater to user interests and even reinforce existing interests in the feedback loop, leading to problems …

TriRank

2015

Most existing collaborative filtering techniques have focused on modeling the binary relation of users to items by extracting from user ratings. Aside from users' ratings, their affiliated reviews often provide the rationale for their ratings …

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, …

A Generic Coordinate Descent Framework for Learning from Implicit Feedback

2017

In recent years, interest in recommender research has shifted from explicit feedback towards implicit feedback data. A diversity of complex models has been proposed for a wide variety of applications. Despite this, learning from implicit …

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 …

Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence Architectures

2018

Existing solutions to task-oriented dialogue systems follow pipeline designs which introduce architectural complexity and fragility. We propose a novel, holistic, extendable framework based on a single sequence-to-sequence (seq2seq) model which can be optimized with supervised …

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, …

A^3NCF: An Adaptive Aspect Attention Model for Rating Prediction

2018

Current recommender systems consider the various aspects of items for making accurate recommendations. Different users place different importance to these aspects which can be thought of as a preference/attention weight vector. Most existing recommender systems …

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 …

Deep Item-based Collaborative Filtering for Top-N Recommendation

2019 · ACM Transactions on Information Systems

Item-based Collaborative Filtering (ICF) has been widely adopted in recommender systems in industry, owing to its strength in user interest modeling and ease in online personalization. By constructing a user’s profile with the items that …

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 …

Neural Graph Collaborative Filtering

2019

Learning vector representations (aka. embeddings) of users and items lies at the core of modern recommender systems. Ranging from early matrix factorization to recently emerged deep learning based methods, existing efforts typically obtain a user's …

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 …

BiRank: Towards Ranking on Bipartite Graphs

2016 · IEEE Transactions on Knowledge and Data Engineering

The bipartite graph is a ubiquitous data structure that can model the relationship between two entity types: for instance, users and items, queries and webpages. In this paper, we study the problem of ranking vertices …

Relational Collaborative Filtering

2019

Existing item-based collaborative filtering (ICF) methods leverage only the relation of collaborative similarity - i.e., the item similarity evidenced by user interactions like ratings and purchases. Nevertheless, there exist multiple relations between items in real-world …

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 …

Sampler Design for Bayesian Personalized Ranking by Leveraging View Data

2019 · IEEE Transactions on Knowledge and Data Engineering

Bayesian Personalized Ranking (BPR) is a representative pairwise learning method for optimizing recommendation models. It is widely known that the performance of BPR depends largely on the quality of negative sampler. In this paper, we …

Reinforced Negative Sampling for Recommendation with Exposure Data

2019

In implicit feedback-based recommender systems, user exposure data, which record whether or not a recommended item has been interacted by a user, provide an important clue on selecting negative training samples. In this work, we …

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. …

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

2020 · arXiv (Cornell University)

Graph Convolution Network (GCN) has become new state-of-the-art for collaborative filtering. Nevertheless, the reasons of its effectiveness for recommendation are not well understood. Existing work that adapts GCN to recommendation lacks thorough ablation analyses on …

Multi-behavior Recommendation with Graph Convolutional Networks

2020

Traditional recommendation models that usually utilize only one type of user-item interaction are faced with serious data sparsity or cold start issues. Multi-behavior recommendation taking use of multiple types of user-item interactions, such as clicks …

Disentangled Graph Collaborative Filtering

2020

Learning informative representations of users and items from the interaction data is of crucial importance to collaborative filtering (CF). Present embedding functions exploit user-item relationships to enrich the representations, evolving from a single user-item instance …

LightGCN

2020

Graph Convolution Network (GCN) has become new state-of-the-art for collaborative filtering. Nevertheless, the reasons of its effectiveness for recommendation are not well understood. Existing work that adapts GCN to recommendation lacks thorough ablation analyses on …

Bias and Debias in Recommender System: A Survey and Future Directions

2022 · ACM Transactions on Information Systems

While recent years have witnessed a rapid growth of research papers on recommender system (RS) , most of the papers focus on inventing machine learning models to better fit user behavior data. However, user behavior …

Self-supervised Graph Learning for Recommendation

2021

Representation learning on user-item graph for recommendation has evolved from using single ID or interaction history to exploiting higher-order neighbors. This leads to the success of graph convolution networks (GCNs) for recommendation such as PinSage …

Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System

2021

The general aim of the recommender system is to provide personalized suggestions to users, which is opposed to suggesting popular items. However, the normal training paradigm, i.e., fitting a recommender model to recover the user …

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

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, …

AutoDebias: Learning to Debias for Recommendation

2021

Recommender systems rely on user behavior data like ratings and clicks to build personalization model. However, the collected data is observational rather than experimental, causing various biases in the data which significantly affect the learned …

Disentangling User Interest and Conformity for Recommendation with Causal Embedding

2021

Recommendation models are usually trained on observational interaction data. However, observational interaction data could result from users’ conformity towards popular items, which entangles users’ real interest. Existing methods tracks this problem as eliminating popularity bias, …

Causal Intervention for Leveraging Popularity Bias in Recommendation

2021

Recommender system usually faces popularity bias issues: from the data perspective, items exhibit uneven (usually long-tail) distribution on the interaction frequency; from the method perspective, collaborative filtering methods are prone to amplify the bias by …

A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation

2022 · IEEE Transactions on Knowledge and Data Engineering

Influenced by the great success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on neural networks. In recent years, we have witnessed significant …

Graph Neural Networks for Recommender System

2022 · Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

Recently, graph neural network (GNN) has become the new state-of-the-art approach in many recommendation problems, with its strong ability to handle structured data and to explore high-order information. However, as the recommendation tasks are diverse …

KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos

2022 · Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Recommender systems deployed in real-world applications can have inherent exposure bias, which leads to the biased logged data plaguing the researchers. A fundamental way to address this thorny problem is to collect users' interactions on …

A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions

2023 · ACM Transactions on Recommender Systems

Recommender system is one of the most important information services on today’s Internet. Recently, graph neural networks have become the new state-of-the-art approach to recommender systems. In this survey, we conduct a comprehensive review of …

TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation

2023

Large Language Models (LLMs) have demonstrated remarkable performance across diverse domains, thereby prompting researchers to explore their potential for use in recommendation systems. Initial attempts have leveraged the exceptional capabilities of LLMs, such as rich …