On the public citation graph

Causal Disentangled Recommendation against User Preference Shifts

https://doi.org/10.1145/3593022
0 signed · 46 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-1-4612-1276-8openalex-snapshot
Monte Carlo Methods in Bayesian Computation
inferredcites →https://doi.org/10.1016/j.eng.2018.02.005openalex-snapshot
Toward Privacy-Preserving Personalized Recommendation Services
inferredcites →https://doi.org/10.1016/j.eswa.2018.09.010openalex-snapshot
Modelling and analysis of temporal preference drifts using a component-based factorised latent approach
inferredcites →https://doi.org/10.1016/j.eswa.2021.115626openalex-snapshot
A novel temporal recommender system based on multiple transitions in user preference drift and topic review evolution
inferredcites →https://doi.org/10.1016/j.ins.2021.05.048openalex-snapshot
Session-aware recommendation: A surprising quest for the state-of-the-art
inferredcites →https://doi.org/10.1016/j.xinn.2024.100590openalex-snapshot
A survey on causal inference for recommendation
inferredcites →https://doi.org/10.1090/noti1912openalex-snapshot
The Book of Why: The New Science of Cause and Effect
inferredcites →https://doi.org/10.1109/access.2021.3128769openalex-snapshot
Personalized Preference Drift Aware Sequential Recommender System
inferredcites →https://doi.org/10.1109/jproc.2021.3058954openalex-snapshot
Toward Causal Representation Learning
inferredcites →https://doi.org/10.1109/tkde.2016.2580511openalex-snapshot
Adapting to User Interest Drift for POI Recommendation
inferredcites →https://doi.org/10.1145/3289600.3291007openalex-snapshot
Sequential Variational Autoencoders for Collaborative Filtering
inferredcites →https://doi.org/10.1145/3298988openalex-snapshot
A Context-Aware User-Item Representation Learning for Item Recommendation
inferredcites →https://doi.org/10.1145/3336191.3371783openalex-snapshot
Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback
inferredcites →https://doi.org/10.1145/3366423.3380037openalex-snapshot
Large-scale Causal Approaches to Debiasing Post-click Conversion Rate Estimation with Multi-task Learning
inferredcites →https://doi.org/10.1145/3458509openalex-snapshot
The Simpson’s Paradox in the Offline Evaluation of Recommendation Systems
inferredcites →https://doi.org/10.1145/3467023openalex-snapshot
Collaborative Reflection-Augmented Autoencoder Network for Recommender Systems
inferredcites →https://doi.org/10.1145/3473972openalex-snapshot
Learning a Hierarchical Intent Model for Next-Item Recommendation
inferredcites →https://doi.org/10.1145/3490476openalex-snapshot
Personalized and Explainable Employee Training Course Recommendations: A Bayesian Variational Approach
inferredcites →https://doi.org/10.1145/3639048openalex-snapshot
Causal Inference in Recommender Systems: A Survey and Future Directions
inferredcites →https://doi.org/10.1145/3714430openalex-snapshot
Causal Inference for Recommendation: Foundations, Methods, and Applications
inferredcites →https://doi.org/10.1145/371920.372071openalex-snapshot
Item-based collaborative filtering recommendation algorithms
inferredcites →https://doi.org/10.1198/016214504000001880openalex-snapshot
Causal Inference Using Potential Outcomes
inferredcites →https://doi.org/10.48550/arxiv.2301.00910openalex-snapshot
Causal Inference in Recommender Systems: A Survey of Strategies for Bias Mitigation, Explanation, and Generalization

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/cpe.70371openalex-snapshot
MobileDiffRec : Out‐of‐Distribution Mobile Application Recommendation Based on Causal Diffusion Graph Representation Learning
inferred← citeshttps://doi.org/10.1007/978-981-95-7394-3_8openalex-snapshot
Causal Insights for Debiasing Recommender Systems: Future Directions and Challenges
inferred← citeshttps://doi.org/10.1007/978-981-97-5555-4_16openalex-snapshot
Cross-Domain Causal Preference Learning for Out-of-Distribution Recommendation
inferred← citeshttps://doi.org/10.1007/978-981-97-5779-4_36openalex-snapshot
What if User Preferences Shifts: Causal Disentanglement for News Recommendation
inferred← citeshttps://doi.org/10.1007/s00521-024-09447-xopenalex-snapshot
Online content-based sequential recommendation considering multimodal contrastive representation and dynamic preferences
inferred← citeshttps://doi.org/10.1007/s11280-024-01242-xopenalex-snapshot
Invariant representation learning to popularity distribution shift for recommendation
inferred← citeshttps://doi.org/10.1016/j.compeleceng.2025.110121openalex-snapshot
Self-supervised graph transformer networks for social recommendation
inferred← citeshttps://doi.org/10.1016/j.eswa.2026.131288openalex-snapshot
Disentangling confounders via counterfactual interventions for fair recommendations
inferred← citeshttps://doi.org/10.1109/iccste65902.2025.11138266openalex-snapshot
Temporal Dynamics Enhanced Sequential Recommendation with Adaptive Attention Mechanism
inferred← citeshttps://doi.org/10.1109/tce.2025.3624922openalex-snapshot
A Distributed Robust Out-of-Distribution Consumer Recommendation System Using Diffusion Model
inferred← citeshttps://doi.org/10.1109/tcss.2025.3603087openalex-snapshot
Enhancing Explainable Sequential Recommendation With Disentangled Representations and Auxiliary Review Explanations
inferred← citeshttps://doi.org/10.1109/tkde.2025.3638343openalex-snapshot
Graph-Structured Driven Dual Adaptation for Mitigating Popularity Bias
inferred← citeshttps://doi.org/10.1145/3696410.3714848openalex-snapshot
Distributionally Robust Graph Out-of-Distribution Recommendation via Diffusion Model
inferred← citeshttps://doi.org/10.1145/3696410.3714849openalex-snapshot
Graph Representation Learning via Causal Diffusion for Out-of-Distribution Recommendation
inferred← citeshttps://doi.org/10.1145/3706633openalex-snapshot
Efficient and Adaptive Recommendation Unlearning: A Guided Filtering Framework to Erase Outdated Preferences
inferred← citeshttps://doi.org/10.1145/3722553openalex-snapshot
Intrinsic and Extrinsic Factor Disentanglement for Recommendation in Various Context Scenarios
inferred← citeshttps://doi.org/10.1145/3723008openalex-snapshot
Disentangling User Interest and Geographical Context for POI Recommendations
inferred← citeshttps://doi.org/10.1145/3746252.3761385openalex-snapshot
Incremental Learning for LLM-based Tokenization and Recommendation
inferred← citeshttps://doi.org/10.1145/3750725openalex-snapshot
Bottlenecked Heterogeneous Graph Contrastive Learning for Robust Recommendation
inferred← citeshttps://doi.org/10.1145/3774935.3803062openalex-snapshot
User-Conditioned Personalized Side-Information in Sequential Recommendation
inferred← citeshttps://doi.org/10.1145/3778863openalex-snapshot
CoDeR+: Interest-aware Counterfactual Reasoning for Sequential Recommendation
inferred← citeshttps://doi.org/10.1145/3829076openalex-snapshot
A Trust and User Preference Model for Marketing Information Dissemination
inferred← citeshttps://doi.org/10.1177/00405175241276794openalex-snapshot
A small neighborhood fabric recommender system based on user historical behavior and preference
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.