2026-11-02 ~ 11-08 · Personalization
Generative · Personalization · AI Search · Infra · Embodied AI
- 1. Beyond IPS: Reliable Counterfactual Evaluation in Multi-Stage Ad Systems without Logged Propensities
Before an ad system launches a new ranking model, it must pick the best candidate offline from logs. In multi-stage delivery, impressions pass through auctions, filters and caching…
Appeared in: 11-02
- 2. PRISM: Personalized Item Representations in User History · 📖 Deep dive
The same cooking video is a pastime for a casual viewer and source material for a food creator. Yet a recommender gives it the same starting embedding in both users' history sequences…
Appeared in: 11-02
- 3. Read More, Reason Deeper: Latent Workspaces for Long-Sequence Recommendation · 📖 Deep dive
Long-sequence recommenders can improve in two ways: read more history, or stack more layers. Current models tie the two together. A sequence encoder pays for every extra event in every layer…
Appeared in: 11-02
- 4. Floor It, Then Fill It: Simulating Creator Ecosystems for Fair Video-Content Recommendation Systems at Billion User Scale
Recommenders allocate traffic by engagement, so big creators keep growing and newcomers struggle to be seen. Testing traffic policies that support newcomers directly in production is costly…
Appeared in: 11-02
- 5. Revisiting Temporal Link Prediction through Structural Proximity
Temporal link prediction guesses who will interact with whom next. Recent methods mostly learn ever more complex temporal representations, yet a classical method can match or beat them…
Appeared in: 11-02
- 6. CASH: A Capability-Aware Subsidy Harness for Advertiser Subsidy Allocation
Ad platforms give advertisers billing discounts, usually set by operators through tier-based rules. Those rules cannot capture how each account responds to different discount levels…
Appeared in: 11-02
- 7. Bridging Semantics and Structure: A Theoretically Grounded Framework for Link Prediction on TAGs
Link prediction on text-attributed graphs, such as predicting whether two papers will cite each other, is usually done by combining a language model with a GNN, but how best to fuse the two is still unsettled…
Appeared in: 11-02
- 8. DeepCommerceRec: a graph- and sequence-aware deep learning recommender system with cold-start adaptation for large-scale e-commerce platforms
E-commerce recommendation has to handle three things at once: long-term collaborative preferences, short-term purchase intent, and new users and items with almost no interactions…
Appeared in: 11-02
- 9. Hidden Disparities in Ranking: Category-Aware Fairness via Distributional Stability
A common fairness check for rankings asks whether different user groups get the same average utility, but this can hide problems. The authors simulate a job platform where the historical data miss women's interest in…
Appeared in: 11-02
- 10. Reinforcement Learning Under Runtime Confounding: Policy Learning and Off-Policy Evaluation
Variables visible in training logs are not always usable at deployment. A clinical decision tool, for example, may be trained with patients' socioeconomic information but barred from using it as an input when deployed…
Appeared in: 11-02