29 citations · 29 across the 9 of their papers we have counts for
9 papers
CaIRec: Calibrated Modality Imputation for Incomplete Multimodal Recommendation
Ruiyu Liu, Xiaohao Liu, Miaomiao Cai +2
Real-world multimodal recommender systems often face incomplete modality observations, where items lack images, text, or other content features. Such incompleteness weakens item re…
ForecastAgentSearch: Towards a Multi-Expert Agent Search System for Geopolitical Event Forecasting
Miaomiao Cai, He Chang, Yunshan Ma +1
Geopolitical event forecasting is a challenging task, as it requires understanding complex regional contexts, dynamic event signals, and uncertain future outcomes. Recent advances…
Dynamic Spectral Denoising with Global-Context Attention for Multi-Behavior Recommendation
Miaomiao Cai, Yunshan Ma, Fangqi Zhu +5
Multi-behavior recommendation improves target-behavior prediction by exploiting heterogeneous auxiliary feedback (e.g., view, collect, and cart), yet its robustness is undermined b…
RMBRec: Robust Multi-Behavior Recommendation towards Target Behaviors
Miaomiao Cai, Zhijie Zhang, Junfeng Fang +3
Multi-behavior recommendation faces a critical challenge in practice: auxiliary behaviors (e.g., clicks, carts) are often noisy, weakly correlated, or semantically misaligned with…
Mitigating Recommendation Biases via Group-Alignment and Global-Uniformity in Representation Learning
Miaomiao Cai, Min Hou, Lei Chen +4
Collaborative Filtering~(CF) plays a crucial role in modern recommender systems, leveraging historical user-item interactions to provide personalized suggestions. However, CF-based…
I-MRec: Invariant Learning with Information Bottleneck for Incomplete Modality Recommendation
Huilin Chen, Miaomiao Cai, Fan Liu +3
Multimodal recommender systems (MRS) improve recommendation performance by integrating complementary semantic information from multiple modalities. However, the assumption of compl…
Graph-Structured Driven Dual Adaptation for Mitigating Popularity Bias
Miaomiao Cai, Lei Chen, Yifan Wang +3
Popularity bias is a common challenge in recommender systems. It often causes unbalanced item recommendation performance and intensifies the Matthew effect. Due to limited user-ite…
Popularity-Aware Alignment and Contrast for Mitigating Popularity Bias
Miaomiao Cai, Lei Chen, Yifan Wang +5
Collaborative Filtering (CF) typically suffers from the significant challenge of popularity bias due to the uneven distribution of items in real-world datasets. This bias leads to…
Multimodality Invariant Learning for Multimedia-Based New Item Recommendation
Haoyue Bai, Le Wu, Min Hou +5
Multimedia-based recommendation provides personalized item suggestions by learning the content preferences of users. With the proliferation of digital devices and APPs, a huge numb…