126 citations · 337 across the 26 of their papers we have counts for
7 papers · 1 filter
Beyond Graph Convolution: Multimodal Recommendation with Topology-aware MLPs
Junjie Huang, Jiarui Qin, Yong Yu +1
Given the large volume of side information from different modalities, multimodal recommender systems have become increasingly vital, as they exploit richer semantic information bey…
Unleashing the Potential of Multi-Channel Fusion in Retrieval for Personalized Recommendations
Junjie Huang, Jiarui Qin, Jianghao Lin +3
Recommender systems (RS) are pivotal in managing information overload in modern digital services. A key challenge in RS is efficiently processing vast item pools to deliver highly…
All Roads Lead to Rome: Unveiling the Trajectory of Recommender Systems Across the LLM Era
Bo Chen, Xinyi Dai, Huifeng Guo +9
Recommender systems (RS) are vital for managing information overload and delivering personalized content, responding to users' diverse information needs. The emergence of large lan…
A Comprehensive Survey on Retrieval Methods in Recommender Systems
Junjie Huang, Jizheng Chen, Jianghao Lin +4
In an era dominated by information overload, effective recommender systems are essential for managing the deluge of data across digital platforms. Multi-stage cascade ranking syste…
M-scan: A Multi-Scenario Causal-driven Adaptive Network for Recommendation
Jiachen Zhu, Yichao Wang, Jianghao Lin +4
We primarily focus on the field of multi-scenario recommendation, which poses a significant challenge in effectively leveraging data from different scenarios to enhance predictions…
Retrieval-Oriented Knowledge for Click-Through Rate Prediction
Huanshuo Liu, Bo Chen, Menghui Zhu +5
Click-through rate (CTR) prediction is crucial for personalized online services. Sample-level retrieval-based models, such as RIM, have demonstrated remarkable performance. However…
D2K: Turning Historical Data into Retrievable Knowledge for Recommender Systems
Jiarui Qin, Weiwen Liu, Ruiming Tang +2
A vast amount of user behavior data is constantly accumulating on today's large recommendation platforms, recording users' various interests and tastes. Preserving knowledge from t…