Not All Videos Become Outdated: Short-Video Recommendation by Learning to Deconfound Release Interval Bias
Abstract
References
Index Terms
- Not All Videos Become Outdated: Short-Video Recommendation by Learning to Deconfound Release Interval Bias
Recommendations
Alleviating Confounding Effects with Contrastive Learning in Recommendation
Advances in Information RetrievalAbstractRecently, there has been a growing interest in mitigating the bias effects in recommendations using causal inference. However, Rubin’s potential outcome framework may produce inaccurate estimates in real-world scenarios due to the presence of ...
Learning Item/User Vectors from Comments for Collaborative Recommendation
ICMLC '17: Proceedings of the 9th International Conference on Machine Learning and ComputingCollaborative Filtering (CF) has been widely used in many recommender systems over the past decades. Conventional CF-based methods mainly consider the ratings given to items via users and suffer from the sparsity and cold-start problems very much. ...
Disentangled causal representation learning for debiasing recommendation with uniform data
AbstractIn recommender systems, learning representations of users and items is a crucial task for predicting user preferences. However, observational data suffer from inherent bias problems. Various confounding factors are present and lead to data biases, ...
Comments
Information & Contributors
Information
Published In

Sponsors
- SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
- SIGAI: ACM Special Interest Group on Artificial Intelligence
- SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
- SIGIR: ACM Special Interest Group on Information Retrieval
- SIGCHI: ACM Special Interest Group on Computer-Human Interaction
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
- Chen Guang project supported by Shanghai Municipal Education Commission and Shanghai Education Development Foundation
- National Natural Science Foundation of China
- Shanghai Pujiang Program
- Fundamental Research Funds for the Central Universities, China
- 2024 Innovation Evaluation Open Fund, Fudan University
- Shanghai Planning Office of Philosophy and Social Science Youth Project
Conference
Acceptance Rates
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 262Total Downloads
- Downloads (Last 12 months)262
- Downloads (Last 6 weeks)16
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format