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cs.IR2024
MLoRA: Multi-Domain Low-Rank Adaptive Network for CTR Prediction
Zhiming Yang, Haining Gao, Dehong Gao +5
Click-through rate (CTR) prediction is one of the fundamental tasks in the industry, especially in e-commerce, social media, and streaming media. It directly impacts website revenu…
cs.IR2024
Modeling User Intent Beyond Trigger: Incorporating Uncertainty for Trigger-Induced Recommendation
Jianxing Ma, Zhibo Xiao, Luwei Yang +5
To cater to users' desire for an immersive browsing experience, numerous e-commerce platforms provide various recommendation scenarios, with a focus on Trigger-Induced Recommendati…
cs.IR2024
Deep Evolutional Instant Interest Network for CTR Prediction in Trigger-Induced Recommendation
Zhibo Xiao, Luwei Yang, Tao Zhang +3
The recommendation has been playing a key role in many industries, e.g., e-commerce, streaming media, social media, etc. Recently, a new recommendation scenario, called Trigger-Ind…