Domain disentanglement with interpolative data augmentation for dual-target cross-domain recommendation
The conventional single-target Cross-Domain Recommendation (CDR) aims to improve the
recommendation performance on a sparser target domain by transferring the knowledge …
recommendation performance on a sparser target domain by transferring the knowledge …
ReFer: Retrieval-Enhanced Vertical Federated Recommendation for Full Set User Benefit
As an emerging privacy-preserving approach to leveraging cross-platform user interactions,
vertical federated learning (VFL) has been increasingly applied in recommender systems …
vertical federated learning (VFL) has been increasingly applied in recommender systems …
RUEL: Retrieval-Augmented User Representation with Edge Browser Logs for Sequential Recommendation
Online recommender systems (RS) aim to match user needs with the vast amount of
resources available on various platforms. A key challenge is to model user preferences …
resources available on various platforms. A key challenge is to model user preferences …
User Feedback-Based Counterfactual Data Augmentation for Sequential Recommendation
The sequential recommendation is a prominent task that aims to provide accurate
recommendations by leveraging users' historical behavior. However, the challenge of data …
recommendations by leveraging users' historical behavior. However, the challenge of data …
ProtoMix: Learnable Data Augmentation on Few-Shot Features with Vector Quantization in CTR Prediction
H Zhao, R Xu, CD Wang, Y Jiang - International Conference on Advanced …, 2023 - Springer
Abstract Click-Through Rate (CTR) prediction is a critical problem in recommendation
systems since it involves enormous business interest. Most deep CTR model follows an …
systems since it involves enormous business interest. Most deep CTR model follows an …