Fuli Feng

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 …

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 User representations will become out-of-date and lead to inappropriate recommendations if user preference has shifted over time.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 …

MaSS: Model-agnostic, Semantic and Stealthy Data Poisoning Attack on Knowledge Graph Embedding

2023

Open-source knowledge graphs are attracting increasing attention. Nevertheless, the openness also raises the concern of data poisoning attacks, that is, the attacker could submit malicious facts to bias the prediction of knowledge graph embedding (KGE) …

Prediction then Correction: An Abductive Prediction Correction Method for Sequential Recommendation

2023

Sequential recommender models typically generate predictions in a single step during testing, without considering additional prediction correction to enhance performance as humans would. To improve the accuracy of these models, some researchers have attempted to …

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 …

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 …

Transferring Causal Mechanism over Meta-representations for Target-Unknown Cross-domain Recommendation

2024 · ACM Transactions on Information Systems

Tackling the pervasive issue of data sparsity in recommender systems, we present an insightful investigation into the burgeoning area of non-overlapping cross-domain recommendation, a technique that facilitates the transfer of interaction knowledge across domains without …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …