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Found 7 papers, 1 papers with code
STAN: Stage-Adaptive Network for Multi-Task Recommendation by Learning User Lifecycle-Based Representation
Our experimental results using both public and industrial datasets demonstrate that the proposed model significantly improves multi-task prediction performance compared to state-of-the-art methods, highlighting the importance of considering user lifecycle stages in recommendation systems.
Knowledge Soft Integration for Multimodal Recommendation
To address this issue, we propose using knowledge soft integration to balance the utilization of multimodal features and the curse of knowledge problem it brings about.
Click-aware Structure Transfer with Sample Weight Assignment for Post-Click Conversion Rate Estimation
To tackle this issue, we argue that a trade-off should be achieved between the introduction of large amounts of auxiliary information and the protection of valuable information related to CVR.
Social4Rec: Distilling User Preference from Social Graph for Video Recommendation in Tencent
Despite recommender systems play a key role in network content platforms, mining the user's interests is still a significant challenge.
Neighbor Based Enhancement for the Long-Tail Ranking Problem in Video Rank Models
Rank models play a key role in industrial recommender systems, advertising, and search engines.
On Modeling Long-Term User Engagement from Stochastic Feedback
An ultimate goal of recommender systems (RS) is to improve user engagement.
NCS4CVR: Neuron-Connection Sharing for Multi-Task Learning in Video Conversion Rate Prediction
To address this sharing&conflict problem, we propose a novel multi-task CVR modeling scheme with neuron-connection level sharing named NCS4CVR, which can automatically and flexibly learn which neuron weights are shared or not shared without artificial experience.
