On generative agents in recommendation
Recommender systems are the cornerstone of today's information dissemination, yet a
disconnect between offline metrics and online performance greatly hinders their …
disconnect between offline metrics and online performance greatly hinders their …
Large language model can interpret latent space of sequential recommender
Sequential recommendation is to predict the next item of interest for a user, based on her/his
interaction history with previous items. In conventional sequential recommenders, a common …
interaction history with previous items. In conventional sequential recommenders, a common …
Llara: Aligning large language models with sequential recommenders
Sequential recommendation aims to predict the subsequent items matching user preference
based on her/his historical interactions. With the development of Large Language Models …
based on her/his historical interactions. With the development of Large Language Models …
Gradient-based bi-level optimization for deep learning: A survey
Bi-level optimization, especially the gradient-based category, has been widely used in the
deep learning community including hyperparameter optimization and meta-knowledge …
deep learning community including hyperparameter optimization and meta-knowledge …
Spatio-Temporal Graphical Counterfactuals: An Overview
Counterfactual thinking is a critical yet challenging topic for artificial intelligence to learn
knowledge from data and ultimately improve their performances for new scenarios. Many …
knowledge from data and ultimately improve their performances for new scenarios. Many …
Data Optimization in Deep Learning: A Survey
O Wu, R Yao - arXiv preprint arXiv:2310.16499, 2023 - arxiv.org
Large-scale, high-quality data are considered an essential factor for the successful
application of many deep learning techniques. Meanwhile, numerous real-world deep …
application of many deep learning techniques. Meanwhile, numerous real-world deep …
PORCA: Root Cause Analysis with Partially Observed Data
Root Cause Analysis (RCA) aims at identifying the underlying causes of system faults by
uncovering and analyzing the causal structure from complex systems. It has been widely …
uncovering and analyzing the causal structure from complex systems. It has been widely …
Causal structure learning for high-dimensional non-stationary time series
S Chen, HT Wu, G Jin - Knowledge-Based Systems, 2024 - Elsevier
Learning the causal structure of high-dimensional non-stationary time series can help in
understanding the data generation mechanism, which is a crucial task in machine learning …
understanding the data generation mechanism, which is a crucial task in machine learning …
Hierarchical topological ordering with conditional independence test for limited time series
Learning directed acyclic graphs (DAGs) to identify causal relations underlying
observational data is crucial but also poses significant challenges. Recently, topology-based …
observational data is crucial but also poses significant challenges. Recently, topology-based …
$\textttcausalAssembly $: Generating Realistic Production Data for Benchmarking Causal Discovery
Algorithms for causal discovery have recently undergone rapid advances and increasingly
draw on flexible nonparametric methods to process complex data. With these advances …
draw on flexible nonparametric methods to process complex data. With these advances …