interest disentanglement

Tencent Advertising Technology

Jul 17, 2024 · Artificial Intelligence

Ads Recommendation in a Collapsed and Entangled World: Tencent's Innovations in Feature Encoding, Dimensional Collapse Mitigation, and Interest Disentanglement

This article summarizes Tencent Advertising's recent research on recommendation models, covering comprehensive feature encoding techniques, solutions to embedding dimensional collapse through multi‑embedding paradigms, and novel methods such as STEM and AME to disentangle conflicting user interests across multiple tasks.

dimensional collapseembeddingfeature encoding

0 likes · 20 min read

Ads Recommendation in a Collapsed and Entangled World: Tencent's Innovations in Feature Encoding, Dimensional Collapse Mitigation, and Interest Disentanglement

DataFunSummit

Jul 14, 2024 · Artificial Intelligence

Causal Inference for Recommender Systems: Disentangling Interest, Conformity, Long‑Term/Short‑Term Interests, and Debiasing Short‑Video Recommendations

This article surveys recent advances in applying causal inference to recommender systems, presenting three lines of work—causal embedding for interest‑conformity disentanglement, contrastive learning for long‑term and short‑term interest separation, and adversarial debiasing of duration bias in short‑video recommendation—along with experimental validation and insights.

Bias Mitigationcausal inferenceinterest disentanglement

0 likes · 24 min read

Causal Inference for Recommender Systems: Disentangling Interest, Conformity, Long‑Term/Short‑Term Interests, and Debiasing Short‑Video Recommendations

Sohu Tech Products

Apr 10, 2024 · Artificial Intelligence

Causal Inference in Recommendation Systems: Disentangling Interests and Debiasing Short Video Recommendations

The presentation surveys recent causal‑inference research for recommendation systems, introducing the DICE framework to separate user interest from conformity, the CLSR model to disentangle long‑term and short‑term preferences, and the DVR approach with WTG metrics to debias short‑video recommendations, demonstrating improved accuracy, fairness, and interpretability.

Bias Mitigationcausal inferenceinterest disentanglement

0 likes · 23 min read

Causal Inference in Recommendation Systems: Disentangling Interests and Debiasing Short Video Recommendations

DataFunTalk

Apr 7, 2024 · Artificial Intelligence

Causal Inference for Recommendation Systems: Disentangling User Interest, Conformity, Long‑Term/Short‑Term Interests, and Debiasing Short‑Video Recommendations

This presentation reviews recent research on applying causal inference to recommendation systems, covering causal embedding for separating user interest and conformity, contrastive learning for disentangling long‑term and short‑term interests, and a debiasing framework for short‑video recommendation that uses watch‑time‑gain metrics and adversarial learning to mitigate duration bias.

Bias Mitigationcausal inferenceinterest disentanglement

0 likes · 23 min read

Causal Inference for Recommendation Systems: Disentangling User Interest, Conformity, Long‑Term/Short‑Term Interests, and Debiasing Short‑Video Recommendations