10 citations · 10 across the 6 of their papers we have counts for
5 papers · 1 filter
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…
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…
Robust Interaction-Based Relevance Modeling for Online e-Commerce Search
Ben Chen, Huangyu Dai, Xiang Ma +2
Semantic relevance calculation is crucial for e-commerce search engines, as it ensures that the items selected closely align with customer intent. Inadequate attention to this aspe…
On Practical Diversified Recommendation with Controllable Category Diversity Framework
Tao Zhang, Luwei Yang, Zhibo Xiao +2
Recommender systems have made significant strides in various industries, primarily driven by extensive efforts to enhance recommendation accuracy. However, this pursuit of accuracy…
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…