2026-10-12 ~ 10-18 · Personalization
Generative · Personalization · AI Search · Agentic · Infra
- 1. Learning What to Keep: Transfer-Aware Personal Skill Learning from User Feedback
Feedback mixes lasting preferences with one-off corrections. SkillGrain learns which atomic rules to retain and jointly trains skill induction and response generation. On one backbone…
Appeared in: 10-16
- 2. Bridging the Topological Divide: Dual-Branch Graph Distillation with Semantic Priors for Robust Cold-Start Recommendation · 📖 Deep dive
DualGCN gives interaction-free items a representation through semantic-anchored graph distillation. A separate student learns directions, while inference restores vector magnitude…
Appeared in: 10-16
- 3. Revisiting Generative vs. Dense Retrieval: A Static Value Prior Reverses the Gap
A controlled study separates exact-target recall, best-item value and whole-list value. A static value prior reverses generative retrieval’s apparent value lead; on industrial data it improves dense retrieval NDCG@50…
Appeared in: 10-16
- 4. PersEvalBench: Can LLM Evaluators Judge Contextually Appropriate Use of Persona Information?
PersEvalBench tests whether evaluators can judge the appropriate use of user history through controlled response pairs. On the human-audited subset, the strongest reported judge detects only 63.9% of…
Appeared in: 10-16
- 5. Learning Individual Scientific Taste with Privileged Hindsight Distillation
The study separates shared, evidence-grounded proposal assessments from individual preference mappings. Hindsight is available only during teacher training. The complete method reaches 0.693 on the reported…
Appeared in: 10-16
- 6. xLightGCN: Explainable Graph Neural Network Based Recommendation Systems with Most Influential Peers
xLightGCN decomposes LightGCN-family scores into influential graph peers and quantifies unexplained residuals. Gowalla experiments average each setting over 100 user-item pairs…
Appeared in: 10-16
- 7. Calibration and Risk-Budgeted Decisions with System One Models
The paper calibrates binary decisions with risk budgets and conservative fallback actions. Across six moderation, safety and reranking datasets, Learn Then Test exceeds the target risk in 7.8% of settings with defined…
Appeared in: 10-16
- 8. MgRec: Multi-granularity intent interaction based sequential recommendation method
MgRec combines Transformer-based sequential preferences with coarse-grained intents extracted through a knowledge graph and a neural topic model. The publisher abstract claims stronger recommendations and reduced…
Appeared in: 10-16
- 9. Similarity-aware generalization framework for zero-shot cross-domain sequential recommendation
SAGERec selectively aligns semantically compatible items and adapts regularization to domain discrepancy for zero-shot cross-domain sequential recommendation. The publisher abstract reports gains but gives no numerical…
Appeared in: 10-16
- 10. Intern-Decision: Efficient Multimodal Decision Making with Autoregressive Masked Language Modeling
Intern-Decision returns structured closed-set choices and candidate probabilities in one causal forward pass. Its 4B model averages 90.02% accuracy across seven evaluation suites…
Appeared in: 10-16