arXiv AI
Eval4DiRec: A Unified and Systematic Evaluation Framework for Diffusion-based Recommender Systems
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arXiv Machine Learning
Sep 3
Training seeds and model-selection stability in recommender-system evaluation
The paper investigates how the choice of random training seed affects recommender‑system experiments. By fixing the data split and varying seeds across hyperparameter settings, the authors analyze seed effects on user‑level metrics, validation‑based model selection, and recommendation‑list agreement. Their findings show that seed variation can be detectable and its impact depends on configuration separation, validation‑to‑test transfer, and top‑k list similarity, indicating that single‑seed results may overstate evaluation stability.
arXiv AI
Jun 2
Time-Aware Diffusion based on Preference Disentanglement for Generative Recommendation
arXiv:2606. 01670v1 Announce Type: cross Abstract: Recently, Generative Recommenders (GRs) have emerged as a transformative recommendation paradigm by replacing traditional item IDs with semantic indices (SIDs).
arXiv Machine Learning
Jul 14
ZoRRO: A Zero-Weight Personalized Recommender System for Scalable News Recommendation
arXiv:2607. 10910v1 Announce Type: cross Abstract: We present ZoRRO (Zero-Weight Personalized Recommender System), a zero-weight, training-free framework for personalized news recommendation designed for scalable real-world deployment.
arXiv AI
Sep 30
FairDiff: Mitigating the Self-Reinforcing Matthew Effect in Diffusion Recommender Models
FairDiff is a new fairness‑aware diffusion framework designed to mitigate the self‑reinforcing Matthew Effect in Diffusion Recommender Models (DRMs). It introduces Popularity Condition Guidance (PCG) to reweight inference‑time gradients and penalize high‑popularity items, and a Semantic Calibration (SC) module that aligns forward and reverse distributions via optimal transport. Experiments show FairDiff achieves state‑of‑the‑art performance while reducing popularity bias in DRMs.
arXiv AI
Aug 10
Towards a Theoretical Understanding of Two Tower Recommendation Models
arXiv:2403. 00802v2 Announce Type: replace-cross Abstract: Production-grade recommender systems rely heavily on a large-scale corpus used by online media services, including Netflix, Pinterest, and Amazon.
arXiv AI
Sep 18
Reproducing Transparent and Scrutable Recommendations: Exploring Open-Weight Models via Natural-Language User Profiles
This reproducibility study confirms that incorporating generated natural‑language user profiles into recommender systems enhances transparency and allows users to directly intervene by correcting preferences or addressing cold‑start issues. The authors replicated the original findings and extended the evaluation with context ablation, multi‑seed stability tests, and mechanistic interpretability analysis using nnsight. Their results show that while perturbing profiles shifts predicted ratings uniformly across genres, the overall rankings remain unchanged, attributing this to the rating‑regression objective rather than the profile interface.