most citedCausalMMM: Learning Causal Structure for Marketing Mix Modeling

9 citations · 11 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI2025

GTool: Graph Enhanced Tool Planning with Large Language Model

Wenjie Chen, Wenbin Li, Di Yao +3

Tool planning with large language models (LLMs), referring to selecting, organizing, and preparing the tools necessary to complete a user request, bridges the gap between natural l…

cs.LG2024

CausalTAD: Causal Implicit Generative Model for Debiased Online Trajectory Anomaly Detection

Wenbin Li, Di Yao, Chang Gong +6

Trajectory anomaly detection, aiming to estimate the anomaly risk of trajectories given the Source-Destination (SD) pairs, has become a critical problem for many real-world applica…

cs.AI2024

PORCA: Root Cause Analysis with Partially Observed Data

Chang Gong, Di Yao, Jin Wang +6

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 used…

cs.CL20242 cited

STBench: Assessing the Ability of Large Language Models in Spatio-Temporal Analysis

Wenbin Li, Di Yao, Ruibo Zhao +7

The rapid evolution of large language models (LLMs) holds promise for reforming the methodology of spatio-temporal data mining. However, current works for evaluating the spatio-tem…

cs.AI20249 cited

CausalMMM: Learning Causal Structure for Marketing Mix Modeling

Chang Gong, Di Yao, Lei Zhang +4

In online advertising, marketing mix modeling (MMM) is employed to predict the gross merchandise volume (GMV) of brand shops and help decision-makers to adjust the budget allocatio…