Pengfei Wang

Stable Learning via Self-supervised Invariant Risk Minimization

2020 · arXiv (Cornell University)

Empirical Risk Minimization based methods are based on the consistency hypothesis that all data samples are generated i.i.d. However, this hypothesis cannot hold in many real-world applications. Consequently, simply minimizing training loss can lead the …

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 …

Dynamic and Adaptive Feature Generation with LLM

2024 · arXiv (Cornell University)

The representation of feature space is a crucial environment where data points get vectorized and embedded for subsequent modeling. Thus the efficacy of machine learning (ML) algorithms is closely related to the quality of feature …

Deep Cut-informed Graph Embedding and Clustering

2025 · arXiv (Cornell University)

Graph clustering aims to divide the graph into different clusters. The recently emerging deep graph clustering approaches are largely built on graph neural networks (GNN). However, GNN is designed for general graph encoding and there …

Learning Hierarchical Representation Model for NextBasket Recommendation

2015

Next basket recommendation is a crucial task in market basket analysis. Given a user's purchase history, usually a sequence of transaction data, one attempts to build a recommender that can predict the next few items …

RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms

2021

In recent years, there are a large number of recommendation algorithms proposed in the literature, from traditional collaborative filtering to deep learning algorithms. However, the concerns about how to standardize open source implementation of recommendation …